Which Gender/Race has more wage gap in Miami Dade County?
Introduction
Loading Libraries and tyding the data
library(readr)## Warning: package 'readr' was built under R version 3.5.3library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.5.3## -- Attaching packages ----------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.1 v purrr 0.3.2
## v tibble 2.1.1 v dplyr 0.8.0.1
## v tidyr 0.8.3 v stringr 1.4.0
## v ggplot2 3.1.1 v forcats 0.4.0## Warning: package 'ggplot2' was built under R version 3.5.3## Warning: package 'tibble' was built under R version 3.5.3## Warning: package 'tidyr' was built under R version 3.5.3## Warning: package 'purrr' was built under R version 3.5.3## Warning: package 'dplyr' was built under R version 3.5.3## Warning: package 'stringr' was built under R version 3.5.3## Warning: package 'forcats' was built under R version 3.5.3## -- Conflicts -------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()setwd("D:/final")
Sal <- read_csv("Sal.csv")## Parsed with column specification:
## cols(
## year = col_double(),
## geo_name = col_character(),
## geo = col_character(),
## soc_name = col_character(),
## soc = col_character(),
## sex_name = col_character(),
## sex = col_double(),
## num_ppl = col_double(),
## num_ppl_moe = col_double(),
## avg_wage_ft = col_double(),
## avg_wage_ft_moe = col_double()
## )race <- read_csv("race.csv")## Parsed with column specification:
## cols(
## year = col_double(),
## geo_name = col_character(),
## geo = col_character(),
## soc_name = col_character(),
## soc = col_character(),
## race_name = col_character(),
## race = col_double(),
## num_ppl = col_double(),
## num_ppl_moe = col_double(),
## avg_wage = col_double(),
## avg_wage_moe = col_double()
## )employ1 <- read_csv("employ1.csv")## Parsed with column specification:
## cols(
## DATE = col_character(),
## unemployment_rate = col_double()
## )salfullrows <- read_csv("salfullrows.csv")## Parsed with column specification:
## cols(
## year = col_double(),
## industry = col_character(),
## agegrp = col_character(),
## quarter = col_double(),
## sex = col_character(),
## EmpTotal = col_double(),
## mon_wage = col_double(),
## Payroll_sum = col_double()
## )racefullrows <- read_csv("racefullrows.csv")## Parsed with column specification:
## cols(
## race = col_character(),
## ethnicity = col_character(),
## year = col_double(),
## quarter1 = col_double(),
## EmpTotal = col_double(),
## EarnS = col_double(),
## Payroll = col_double()
## )predictedrace <- read_csv("predicted race.csv")## Parsed with column specification:
## cols(
## Year = col_double(),
## Race = col_character(),
## mean = col_double(),
## sex = col_character(),
## gender_mean = col_double(),
## year1 = col_double(),
## Unemploy = col_double(),
## year2 = col_double()
## )newemploy<- drop_na(employ1)
data(newemploy)## Warning in data(newemploy): data set 'newemploy' not foundsummary(newemploy)## DATE unemployment_rate
## Length:350 Min. : 2.500
## Class :character 1st Qu.: 4.500
## Mode :character Median : 5.900
## Mean : 6.259
## 3rd Qu.: 7.875
## Max. :12.400print(newemploy)## # A tibble: 350 x 2
## DATE unemployment_rate
## <chr> <dbl>
## 1 1/1/1990 6.6
## 2 2/1/1990 6.7
## 3 3/1/1990 7
## 4 4/1/1990 6.9
## 5 5/1/1990 7
## 6 6/1/1990 7.2
## 7 7/1/1990 7.7
## 8 8/1/1990 7.9
## 9 9/1/1990 8.2
## 10 10/1/1990 7.8
## # ... with 340 more rowsTimeseries
library(forecast)## Warning: package 'forecast' was built under R version 3.5.3library(readr)
library(dplyr)
newemploy$DATE <- as.Date(newemploy$DATE)
library(zoo)## Warning: package 'zoo' was built under R version 3.5.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numerictsdata <- ts(newemploy$unemployment_rate,start= 1990,end= 2018,frequency = 12)
# look at the trend
ddata <- decompose(tsdata)
plot(ddata)
plot(ddata$seasonal)
plot(ddata$trend)
class(tsdata)## [1] "ts"plot(newemploy$unemployment_rate,type="l",main="Time vs Unemployment rate")
#Arima model
mymodel <- auto.arima(tsdata)
mymodel## Series: tsdata
## ARIMA(1,1,1)(2,0,0)[12]
##
## Coefficients:
## ar1 ma1 sar1 sar2
## 0.8435 -0.7164 0.3373 0.2951
## s.e. 0.0701 0.0881 0.0520 0.0548
##
## sigma^2 estimated as 0.09186: log likelihood=-76.4
## AIC=162.8 AICc=162.98 BIC=181.89fitting the models
auto.arima(tsdata,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,1,2)(1,0,1)[12] with drift : Inf
## ARIMA(0,1,0) with drift : 275.9748
## ARIMA(1,1,0)(1,0,0)[12] with drift : 186.6235
## ARIMA(0,1,1)(0,0,1)[12] with drift : 225.276
## ARIMA(0,1,0) : 274.1171
## ARIMA(1,1,0) with drift : 277.5938
## ARIMA(1,1,0)(2,0,0)[12] with drift : 153.9113
## ARIMA(1,1,0)(2,0,1)[12] with drift : 149.3164
## ARIMA(1,1,0)(1,0,1)[12] with drift : Inf
## ARIMA(1,1,0)(2,0,2)[12] with drift : Inf
## ARIMA(1,1,0)(1,0,2)[12] with drift : Inf
## ARIMA(0,1,0)(2,0,1)[12] with drift : 160.4163
## ARIMA(2,1,0)(2,0,1)[12] with drift : 139.3738
## ARIMA(2,1,0)(1,0,1)[12] with drift : Inf
## ARIMA(2,1,0)(2,0,0)[12] with drift : 148.479
## ARIMA(2,1,0)(2,0,2)[12] with drift : Inf
## ARIMA(2,1,0)(1,0,0)[12] with drift : 185.3063
## ARIMA(2,1,0)(1,0,2)[12] with drift : Inf
## ARIMA(3,1,0)(2,0,1)[12] with drift : 135.6865
## ARIMA(3,1,0)(1,0,1)[12] with drift : Inf
## ARIMA(3,1,0)(2,0,0)[12] with drift : 140.3359
## ARIMA(3,1,0)(2,0,2)[12] with drift : Inf
## ARIMA(3,1,0)(1,0,0)[12] with drift : 179.438
## ARIMA(3,1,0)(1,0,2)[12] with drift : Inf
## ARIMA(4,1,0)(2,0,1)[12] with drift : 137.4434
## ARIMA(3,1,1)(2,0,1)[12] with drift : 137.0394
## ARIMA(2,1,1)(2,0,1)[12] with drift : 135.3993
## ARIMA(2,1,1)(1,0,1)[12] with drift : Inf
## ARIMA(2,1,1)(2,0,0)[12] with drift : 140.8232
## ARIMA(2,1,1)(2,0,2)[12] with drift : Inf
## ARIMA(2,1,1)(1,0,0)[12] with drift : 186.155
## ARIMA(2,1,1)(1,0,2)[12] with drift : Inf
## ARIMA(1,1,1)(2,0,1)[12] with drift : 132.8724
## ARIMA(1,1,1)(1,0,1)[12] with drift : Inf
## ARIMA(1,1,1)(2,0,0)[12] with drift : 141.5802
## ARIMA(1,1,1)(2,0,2)[12] with drift : Inf
## ARIMA(1,1,1)(1,0,0)[12] with drift : 184.3865
## ARIMA(1,1,1)(1,0,2)[12] with drift : Inf
## ARIMA(0,1,1)(2,0,1)[12] with drift : 153.907
## ARIMA(1,1,2)(2,0,1)[12] with drift : 133.3221
## ARIMA(0,1,2)(2,0,1)[12] with drift : 148.8814
## ARIMA(2,1,2)(2,0,1)[12] with drift : 137.0888
## ARIMA(1,1,1)(2,0,1)[12] : 130.8857
## ARIMA(1,1,1)(1,0,1)[12] : Inf
## ARIMA(1,1,1)(2,0,0)[12] : 139.6814
## ARIMA(1,1,1)(2,0,2)[12] : Inf
## ARIMA(1,1,1)(1,0,0)[12] : 182.7696
## ARIMA(1,1,1)(1,0,2)[12] : Inf
## ARIMA(0,1,1)(2,0,1)[12] : 152.4477
## ARIMA(1,1,0)(2,0,1)[12] : 147.6907
## ARIMA(2,1,1)(2,0,1)[12] : 133.4222
## ARIMA(1,1,2)(2,0,1)[12] : 131.3617
## ARIMA(0,1,0)(2,0,1)[12] : 159.0808
## ARIMA(0,1,2)(2,0,1)[12] : 147.383
## ARIMA(2,1,0)(2,0,1)[12] : 137.5097
## ARIMA(2,1,2)(2,0,1)[12] : 135.1501
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,1,1)(2,0,1)[12] : Inf
## ARIMA(1,1,2)(2,0,1)[12] : Inf
## ARIMA(1,1,1)(2,0,1)[12] with drift : Inf
## ARIMA(1,1,2)(2,0,1)[12] with drift : Inf
## ARIMA(2,1,1)(2,0,1)[12] : Inf
## ARIMA(2,1,2)(2,0,1)[12] : Inf
## ARIMA(2,1,1)(2,0,1)[12] with drift : Inf
## ARIMA(3,1,0)(2,0,1)[12] with drift : Inf
## ARIMA(3,1,1)(2,0,1)[12] with drift : Inf
## ARIMA(2,1,2)(2,0,1)[12] with drift : Inf
## ARIMA(4,1,0)(2,0,1)[12] with drift : Inf
## ARIMA(2,1,0)(2,0,1)[12] : Inf
## ARIMA(2,1,0)(2,0,1)[12] with drift : Inf
## ARIMA(1,1,1)(2,0,0)[12] : 162.8024
##
## Best model: ARIMA(1,1,1)(2,0,0)[12]## Series: tsdata
## ARIMA(1,1,1)(2,0,0)[12]
##
## Coefficients:
## ar1 ma1 sar1 sar2
## 0.8435 -0.7164 0.3373 0.2951
## s.e. 0.0701 0.0881 0.0520 0.0548
##
## sigma^2 estimated as 0.09186: log likelihood=-76.4
## AIC=162.8 AICc=162.98 BIC=181.89residuals
library(tseries)## Warning: package 'tseries' was built under R version 3.5.3adf.test(mymodel$residuals)## Warning in adf.test(mymodel$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel$residuals
## Dickey-Fuller = -6.2557, Lag order = 6, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel$residuals)
acf(ts(mymodel$residuals),main = "ACF Residual")
pacf(ts(mymodel$residuals),main = "PACF Residual")
#forecasting for unemployment
myforecast <- forecast(mymodel, level =c (95), h= 5*12)
plot(myforecast,main = "Unemployment forecasting for the next 5 years",xlab = "Time", ylab ="Unemployment rate")
myforecast[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 2018 4.024446 4.165835 3.996864 3.900939 4.016970 4.041224
## 2019 3.706891 3.650299 3.726152 3.579489 3.546176 3.614015 3.621514
## 2020 3.358837 3.317245 3.384381 3.284899 3.245230 3.302249 3.311848
## 2021 3.124277 3.093520 3.138528 3.061672 3.038444 3.077683 3.083122
## 2022 2.942297 2.919645 2.954635 2.899351 2.879808 2.909868 2.914534
## 2023 2.811675
## Aug Sep Oct Nov Dec
## 2018 4.133759 4.092072 3.966653 3.715247 3.805754
## 2019 3.711173 3.637604 3.565381 3.391704 3.451452
## 2020 3.369323 3.332143 3.270717 3.137898 3.184723
## 2021 3.128958 3.094699 3.052659 2.956600 2.990021
## 2022 2.946955 2.924426 2.892117 2.820520 2.845611
## 2023exponential smoothing forecast
eforecast <- ets(tsdata)
plot(forecast(eforecast,h=5*12))
AVerage for unemployment
Unemployment_rate <- 2.845611
cat("predicted average Unemployment_rate for the next 5 years in Miami Dade County in :",Unemployment_rate)## predicted average Unemployment_rate for the next 5 years in Miami Dade County in : 2.845611forecasting average salary male and females: dataset:1(monthly salary)
library(forecast)
library(readr)
newsalfullrows <- drop_na(salfullrows)
newsalfullrows$mon_wage <- as.numeric(newsalfullrows$mon_wage)
library(zoo)
newsalfullrows$year <- paste (newsalfullrows$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
ns <- ts(newsalfullrows$mon_wage,start= 1997, end= 2018,frequency = 12)
ns1 <- decompose(ns)
plot(ns1)
plot(ns1$seasonal)
plot(ns1$trend)
plot(newsalfullrows$mon_wage,type ="l",main = "monthly salary of both genders",xlab= "Year",ylab="monthly Salary")
class(ns1)## [1] "decomposed.ts"ammodel <- auto.arima(ns)
ammodel## Series: ns
## ARIMA(2,0,0)(2,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 sar1 sar2 mean
## 1.3446 -0.6045 -0.1348 0.4421 1972.9337
## s.e. 0.0516 0.0533 0.0593 0.0597 156.2309
##
## sigma^2 estimated as 224906: log likelihood=-1919.4
## AIC=3850.8 AICc=3851.14 BIC=3872auto.arima(ns,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(0,0,0) with non-zero mean : 4292.38
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3968.832
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 4056.747
## ARIMA(0,0,0) with zero mean : 4643.492
## ARIMA(1,0,0) with non-zero mean : 4075.812
## ARIMA(1,0,0)(2,0,0)[12] with non-zero mean : 3948.25
## ARIMA(1,0,0)(2,0,1)[12] with non-zero mean : Inf
## ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(0,0,0)(2,0,0)[12] with non-zero mean : 4236.349
## ARIMA(2,0,0)(2,0,0)[12] with non-zero mean : 3858.404
## ARIMA(2,0,0)(1,0,0)[12] with non-zero mean : 3894.56
## ARIMA(2,0,0)(2,0,1)[12] with non-zero mean : Inf
## ARIMA(2,0,0)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(3,0,0)(2,0,0)[12] with non-zero mean : 3859.462
## ARIMA(2,0,1)(2,0,0)[12] with non-zero mean : 3859.049
## ARIMA(1,0,1)(2,0,0)[12] with non-zero mean : 3884.906
## ARIMA(3,0,1)(2,0,0)[12] with non-zero mean : 3860.717
## ARIMA(2,0,0)(2,0,0)[12] with zero mean : 3888.17
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(2,0,0)(2,0,0)[12] with non-zero mean : 3850.803
##
## Best model: ARIMA(2,0,0)(2,0,0)[12] with non-zero mean## Series: ns
## ARIMA(2,0,0)(2,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 sar1 sar2 mean
## 1.3446 -0.6045 -0.1348 0.4421 1972.9337
## s.e. 0.0516 0.0533 0.0593 0.0597 156.2309
##
## sigma^2 estimated as 224906: log likelihood=-1919.4
## AIC=3850.8 AICc=3851.14 BIC=3872pred_sal <- forecast(ammodel, level =c (95), h= 5*12)
plot(pred_sal,main = "Monthly Salary forecasting for the next 5years",xlab = "Time", ylab ="monthly salary")
pred_sal[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 2018 2101.752 1981.701 1543.999 1224.128 1331.827 1470.799
## 2019 1233.581 1369.261 1666.505 2095.019 2413.056 2704.334 2692.255
## 2020 2116.617 2110.595 2017.621 1766.577 1582.496 1590.981 1654.111
## 2021 1626.660 1687.457 1831.413 2054.717 2220.149 2347.786 2333.938
## 2022 2083.140 2072.281 2011.769 1870.677 1766.991 1753.534 1783.311
## 2023 1804.988
## Aug Sep Oct Nov Dec
## 2018 1786.758 2110.940 2052.702 1789.938 1498.338
## 2019 2207.144 1231.596 1362.172 1787.897 2023.740
## 2020 1859.199 2134.005 2090.601 1916.989 1756.245
## 2021 2091.820 1623.478 1687.055 1898.672 2024.609
## 2022 1906.624 2091.258 2063.497 1958.213 1870.168
## 2023Forecasting for male monthly salary
library(forecast)
library(readr)
newsalfullrows <- drop_na(salfullrows)
newsalfullrows$mon_wage <- as.numeric(newsalfullrows$mon_wage)
malesalfullrows<- subset(newsalfullrows,sex == "Male",select=c(year,mon_wage))
library(zoo)
malesalfullrows$year <- paste (malesalfullrows$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
malens <- ts(malesalfullrows$mon_wage,start= 1997, end= 2018,frequency = 12)
ns2 <- decompose(malens)
plot(ns2)
plot(ns2$seasonal)
plot(ns2$trend)
plot(malesalfullrows$mon_wage,type ="l",main = "male monthly salary ",xlab= "Year",ylab="monthly Salary")
class(ns2)## [1] "decomposed.ts"ammodel2 <- auto.arima(malens)
ammodel2## Series: malens
## ARIMA(3,0,0) with non-zero mean
##
## Coefficients:
## ar1 ar2 ar3 mean
## 1.1979 -0.6064 -0.1148 2329.3796
## s.e. 0.0630 0.0908 0.0633 69.3817
##
## sigma^2 estimated as 336984: log likelihood=-1968.29
## AIC=3946.57 AICc=3946.81 BIC=3964.24auto.arima(malens,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(0,0,0) with non-zero mean : 4337.875
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 4026.044
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 4110.027
## ARIMA(0,0,0) with zero mean : 4707.917
## ARIMA(1,0,0) with non-zero mean : 4147.831
## ARIMA(1,0,0)(2,0,0)[12] with non-zero mean : Inf
## ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(1,0,0)(0,0,1)[12] with non-zero mean : 4102.343
## ARIMA(1,0,0)(2,0,1)[12] with non-zero mean : Inf
## ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 4222.714
## ARIMA(2,0,0)(1,0,0)[12] with non-zero mean : 3951.643
## ARIMA(2,0,0) with non-zero mean : 3945.51
## ARIMA(2,0,0)(0,0,1)[12] with non-zero mean : 3946.445
## ARIMA(2,0,0)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(3,0,0) with non-zero mean : 3944.358
## ARIMA(3,0,0)(1,0,0)[12] with non-zero mean : 3951.799
## ARIMA(3,0,0)(0,0,1)[12] with non-zero mean : 3945.775
## ARIMA(3,0,0)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(4,0,0) with non-zero mean : 3946.079
## ARIMA(3,0,1) with non-zero mean : 3946.146
## ARIMA(2,0,1) with non-zero mean : 3944.988
## ARIMA(4,0,1) with non-zero mean : 3949.045
## ARIMA(3,0,0) with zero mean : 4050.776
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(3,0,0) with non-zero mean : 3946.571
##
## Best model: ARIMA(3,0,0) with non-zero mean## Series: malens
## ARIMA(3,0,0) with non-zero mean
##
## Coefficients:
## ar1 ar2 ar3 mean
## 1.1979 -0.6064 -0.1148 2329.3796
## s.e. 0.0630 0.0908 0.0633 69.3817
##
## sigma^2 estimated as 336984: log likelihood=-1968.29
## AIC=3946.57 AICc=3946.81 BIC=3964.24pred_sal2 <- forecast(ammodel2, level =c (95), h= 5*12)plot(pred_sal2,main = "male Monthly Salary forecasting for the next 5years",xlab = "Time", ylab ="monthly salary")
pred_sal2[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 2018 5231.4202 3864.8002 2067.0610 750.9499 421.4064 1031.0966
## 2019 2050.1832 1654.9187 1647.6697 1953.8035 2370.2875 2684.3847 2772.9441
## 2020 2234.4692 2387.3447 2482.9858 2489.1285 2420.9417 2324.5576 2249.7428
## 2021 2384.1863 2353.2004 2317.5399 2294.4607 2291.9957 2307.1315 2329.4065
## 2022 2315.0982 2316.4452 2323.4399 2331.7472 2337.3024 2338.1164 2334.7692
## 2023 2331.0619
## Aug Sep Oct Nov Dec
## 2018 2112.3515 3075.6943 3504.0207 3308.8248 2704.6827
## 2019 2640.7530 2392.6447 2165.4318 2058.8812 2097.5065
## 2020 2226.3968 2254.8622 2311.7057 2365.2166 2391.5799
## 2021 2347.1944 2353.2574 2347.1767 2334.1743 2321.5901
## 2022 2329.6284 2325.4065 2323.8508 2325.1374 2328.1066
## 2023forecasting for female monthly salary
library(forecast)
library(readr)
newsalfullrows <- drop_na(salfullrows)
newsalfullrows$mon_wage <- as.numeric(newsalfullrows$mon_wage)
femalesalfullrows<- subset(newsalfullrows,sex == "Female",select=c(year,mon_wage))
library(zoo)
femalesalfullrows$year <- paste (femalesalfullrows$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
femalens <- ts(femalesalfullrows$mon_wage,start= 1997, end= 2018,frequency = 12)
ns3 <- decompose(femalens)
plot(ns3)
plot(ns3$seasonal)
plot(ns3$trend)
plot(femalesalfullrows$mon_wage,type ="l",main = " female monthly salary",xlab= "Year",ylab="monthly Salary")
class(ns3)## [1] "decomposed.ts"ammodel3 <- auto.arima(femalens)
ammodel3## Series: femalens
## ARIMA(5,0,0)(1,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 ar3 ar4 ar5 sar1 mean
## 1.1343 -0.2729 -0.0779 -0.5626 0.6089 0.4862 1636.0760
## s.e. 0.0499 0.0805 0.0821 0.0859 0.0554 0.0621 185.2207
##
## sigma^2 estimated as 77840: log likelihood=-1783.92
## AIC=3583.85 AICc=3584.44 BIC=3612.11auto.arima(femalens,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(0,0,0) with non-zero mean : 4035.823
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3704.982
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3814.333
## ARIMA(0,0,0) with zero mean : 4499.861
## ARIMA(1,0,0) with non-zero mean : 3834.931
## ARIMA(1,0,0)(2,0,0)[12] with non-zero mean : Inf
## ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(1,0,0)(0,0,1)[12] with non-zero mean : 3784.532
## ARIMA(1,0,0)(2,0,1)[12] with non-zero mean : Inf
## ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 3917.132
## ARIMA(2,0,0)(1,0,0)[12] with non-zero mean : 3657.971
## ARIMA(2,0,0) with non-zero mean : 3649.704
## ARIMA(2,0,0)(0,0,1)[12] with non-zero mean : 3651.181
## ARIMA(2,0,0)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(3,0,0) with non-zero mean : 3648.346
## ARIMA(3,0,0)(1,0,0)[12] with non-zero mean : 3655.537
## ARIMA(3,0,0)(0,0,1)[12] with non-zero mean : 3649.868
## ARIMA(3,0,0)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(4,0,0) with non-zero mean : 3642.936
## ARIMA(4,0,0)(1,0,0)[12] with non-zero mean : 3650.218
## ARIMA(4,0,0)(0,0,1)[12] with non-zero mean : 3644.734
## ARIMA(4,0,0)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(5,0,0) with non-zero mean : 3620.184
## ARIMA(5,0,0)(1,0,0)[12] with non-zero mean : 3585.583
## ARIMA(5,0,0)(2,0,0)[12] with non-zero mean : Inf
## ARIMA(5,0,0)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(5,0,0)(0,0,1)[12] with non-zero mean : 3609.814
## ARIMA(5,0,0)(2,0,1)[12] with non-zero mean : Inf
## ARIMA(5,0,1)(1,0,0)[12] with non-zero mean : 3587.316
## ARIMA(4,0,1)(1,0,0)[12] with non-zero mean : 3629.315
## ARIMA(5,0,0)(1,0,0)[12] with zero mean : 3595.367
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(5,0,0)(1,0,0)[12] with non-zero mean : 3583.847
##
## Best model: ARIMA(5,0,0)(1,0,0)[12] with non-zero mean## Series: femalens
## ARIMA(5,0,0)(1,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 ar3 ar4 ar5 sar1 mean
## 1.1343 -0.2729 -0.0779 -0.5626 0.6089 0.4862 1636.0760
## s.e. 0.0499 0.0805 0.0821 0.0859 0.0554 0.0621 185.2207
##
## sigma^2 estimated as 77840: log likelihood=-1783.92
## AIC=3583.85 AICc=3584.44 BIC=3612.11pred_sal3 <- forecast(ammodel3, level =c (95), h= 5*12)
plot(pred_sal3,main = "female Monthly Salary forecasting for the next 5years",xlab = "Time", ylab ="monthly salary")
pred_sal3[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 2018 2832.026 2504.316 1939.245 1396.876 1538.079 1720.615
## 2019 1279.432 1290.414 1675.560 2179.379 2498.042 2577.023 2151.430
## 2020 2266.644 2311.405 2069.107 1675.903 1349.339 1336.993 1519.311
## 2021 1337.244 1299.165 1506.094 1824.608 2063.661 2119.790 1911.486
## 2022 1970.020 2018.125 1872.445 1619.784 1405.274 1364.491 1487.143
## 2023 1426.604
## Aug Sep Oct Nov Dec
## 2018 2027.967 2447.776 2648.321 2207.536 1672.656
## 2019 1562.783 1215.706 1278.317 1528.016 1948.803
## 2020 1823.455 2104.354 2188.744 1952.735 1607.044
## 2021 1585.567 1347.464 1323.235 1478.532 1749.507
## 2022 1706.333 1901.049 1957.836 1829.521 1611.062
## 2023calculating monthly women salary for the year 2020
womensal <- 2391.5799/1337.244
womensal## [1] 1.788439cat("The average mens monthly salary is ",womensal,"times of womens monthly salary")## The average mens monthly salary is 1.788439 times of womens monthly salarycalculating how much women earns for every men who earns a dollar
wcents <- 1/womensal
wcents## [1] 0.5591467cat("The women earns",wcents*100, "cents for every men who earns a dollar")## The women earns 55.91467 cents for every men who earns a dollarforecasting for race 1: White Alone
library(forecast)
library(readr)
newracefullrows <- drop_na(racefullrows)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
wracefullrows<- subset(newracefullrows,race == "White Alone",select=c(year,EarnS))
wracefullrows$year <- paste (wracefullrows$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
wrc <- ts(wracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns4 <- decompose(wrc)
plot(ns4)
plot(ns4$seasonal)
plot(ns4$trend)
plot(wracefullrows$EarnS,type ="l",main = " whites monthly salary",xlab= "Year",ylab="monthly Salary")
class(ns4)## [1] "decomposed.ts"ammodel4 <- auto.arima(wrc)
ammodel4## Series: wrc
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 ma1 ma2 sar1 sma1 mean
## -0.0982 0.5446 0.9489 -0.0213 0.3511 0.2131 2586.3894
## s.e. 0.1121 0.0670 0.1288 0.1099 0.1527 0.1612 196.2576
##
## sigma^2 estimated as 256665: log likelihood=-1933.46
## AIC=3882.93 AICc=3883.52 BIC=3911.19auto.arima(wrc,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3869.468
## ARIMA(0,0,0) with non-zero mean : 4070.132
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3883.139
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3930.748
## ARIMA(0,0,0) with zero mean : 4717.555
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 3891.533
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 3872.735
## ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : Inf
## ARIMA(2,0,2)(1,0,2)[12] with non-zero mean : 3871.372
## ARIMA(2,0,2) with non-zero mean : 3938.785
## ARIMA(2,0,2)(0,0,2)[12] with non-zero mean : 3891.567
## ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : Inf
## ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 3858.476
## ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(2,0,1)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(3,0,2)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(2,0,3)(2,0,2)[12] with non-zero mean : 3860.011
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(1,0,3)(2,0,2)[12] with non-zero mean : 3870.577
## ARIMA(3,0,1)(2,0,2)[12] with non-zero mean : 3859.545
## ARIMA(3,0,3)(2,0,2)[12] with non-zero mean : 3862.444
## ARIMA(2,0,2)(2,0,2)[12] with zero mean : Inf
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(3,0,1)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(2,0,3)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(3,0,3)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3882.928
##
## Best model: ARIMA(2,0,2)(1,0,1)[12] with non-zero mean## Series: wrc
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 ma1 ma2 sar1 sma1 mean
## -0.0982 0.5446 0.9489 -0.0213 0.3511 0.2131 2586.3894
## s.e. 0.1121 0.0670 0.1288 0.1099 0.1527 0.1612 196.2576
##
## sigma^2 estimated as 256665: log likelihood=-1933.46
## AIC=3882.93 AICc=3883.52 BIC=3911.19pred_sal4 <- forecast(ammodel4, level =c (95), h= 3*12)
plot(pred_sal4,main = "whites Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")
pred_sal4[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 2018 2196.714 2232.776 1945.297 2072.246 1955.623 2275.175
## 2019 2623.485 2435.071 2502.752 2349.444 2429.121 2356.200 2490.644
## 2020 2602.256 2531.519 2558.749 2502.087 2532.227 2504.866 2553.418
## 2021 2592.109
## Aug Sep Oct Nov Dec
## 2018 2237.881 2131.209 2765.251 2800.739 2735.714
## 2019 2457.949 2434.551 2645.079 2666.374 2636.112
## 2020 2540.848 2533.479 2606.708 2614.711 2603.666
## 2021forecasting for race 2: Black or African American Alone
library(forecast)
library(readr)
newracefullrows <- drop_na(racefullrows)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
bracefullrows<- subset(newracefullrows,race == "Black or African American Alone",select=c(year,EarnS))
bracefullrows$year <- paste (bracefullrows$year,"/01/01",sep ="")
brc <- ts(bracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns5 <- decompose(brc)
plot(ns5)
plot(ns5$seasonal)
plot(ns5$trend)
plot(bracefullrows$EarnS,type ="l",main = " Blacks monthly salary",xlab= "Year",ylab="monthly Salary")
class(ns5)## [1] "decomposed.ts"ammodel5 <- auto.arima(brc)
ammodel5## Series: brc
## ARIMA(1,0,1)(2,0,1)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 sar1 sar2 sma1 mean
## 0.6212 0.2779 0.8073 -0.3053 -0.6265 1921.688
## s.e. 0.0625 0.0743 0.1304 0.0631 0.1260 61.607
##
## sigma^2 estimated as 137151: log likelihood=-1853.95
## AIC=3721.91 AICc=3722.36 BIC=3746.64auto.arima(brc,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3708.363
## ARIMA(0,0,0) with non-zero mean : 3941.113
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3722.455
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3786.034
## ARIMA(0,0,0) with zero mean : 4568.404
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 3730.33
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 3710.085
## ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 3707.466
## ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 3710.932
## ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 3706.48
## ARIMA(2,0,2)(1,0,2)[12] with non-zero mean : 3710.353
## ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : 3703.852
## ARIMA(1,0,2)(1,0,2)[12] with non-zero mean : 3708.344
## ARIMA(1,0,2)(2,0,1)[12] with non-zero mean : 3704.971
## ARIMA(1,0,2)(1,0,1)[12] with non-zero mean : 3706.346
## ARIMA(0,0,2)(2,0,2)[12] with non-zero mean : 3709.356
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : 3703.073
## ARIMA(1,0,1)(1,0,2)[12] with non-zero mean : 3710.723
## ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 3704.449
## ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 3708.827
## ARIMA(0,0,1)(2,0,2)[12] with non-zero mean : 3757.073
## ARIMA(1,0,0)(2,0,2)[12] with non-zero mean : 3714.132
## ARIMA(2,0,1)(2,0,2)[12] with non-zero mean : 3705.238
## ARIMA(0,0,0)(2,0,2)[12] with non-zero mean : 3910.815
## ARIMA(2,0,0)(2,0,2)[12] with non-zero mean : 3703.261
## ARIMA(1,0,1)(2,0,2)[12] with zero mean : 3742.365
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(2,0,0)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 3721.905
##
## Best model: ARIMA(1,0,1)(2,0,1)[12] with non-zero mean## Series: brc
## ARIMA(1,0,1)(2,0,1)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 sar1 sar2 sma1 mean
## 0.6212 0.2779 0.8073 -0.3053 -0.6265 1921.688
## s.e. 0.0625 0.0743 0.1304 0.0631 0.1260 61.607
##
## sigma^2 estimated as 137151: log likelihood=-1853.95
## AIC=3721.91 AICc=3722.36 BIC=3746.64pred_sal5 <- forecast(ammodel5, level =c (95), h= 3*12)
plot(pred_sal5,main = "blacks Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")
pred_sal5[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 2018 1596.241 1686.469 1681.904 1704.883 1750.282 1716.900
## 2019 1861.512 1838.467 1926.831 2013.939 2018.004 2032.760 1914.195
## 2020 1939.506 1954.204 1997.864 2069.501 2065.717 2063.738 1978.192
## 2021 1954.446
## Aug Sep Oct Nov Dec
## 2018 1720.417 1831.786 1981.476 1983.431 1963.285
## 2019 1914.332 1984.590 1790.800 1790.900 1796.690
## 2020 1977.217 1999.929 1797.776 1797.257 1808.080
## 2021forecasting for race 3: American Indian or Alaska Native Alone
library(forecast)
library(readr)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
aracefullrows <- subset(newracefullrows,race == "American Indian or Alaska Native Alone",select=c(year,EarnS))
head(aracefullrows)aracefullrows$year <- paste(aracefullrows$year,"/01/01",sep ="")
arc <- ts(aracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns6 <- decompose(arc)
plot(ns6)
plot(ns6$seasonal)
plot(ns6$trend)
plot(aracefullrows$EarnS,type ="l",main = "American Indian or Alaska Native Alone monthly salary",xlab= "Year",ylab="monthly Salary")
class(ns6)## [1] "decomposed.ts"ammodel6 <- auto.arima(arc)
ammodel6## Series: arc
## ARIMA(2,0,1)(2,0,1)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 ma1 sar1 sar2 sma1 mean
## -0.1147 0.5533 0.9410 0.7711 -0.1984 -0.3662 2106.3192
## s.e. 0.0717 0.0697 0.0394 0.3031 0.1273 0.2983 136.2093
##
## sigma^2 estimated as 191849: log likelihood=-1895.66
## AIC=3807.32 AICc=3807.91 BIC=3835.59auto.arima(arc,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3791.532
## ARIMA(0,0,0) with non-zero mean : 3992.2
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3795.956
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3856.997
## ARIMA(0,0,0) with zero mean : 4616.041
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 3814.752
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 3790.989
## ARIMA(2,0,2) with non-zero mean : 3838.94
## ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 3787.969
## ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 3786.205
## ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 3783.49
## ARIMA(2,0,2)(1,0,2)[12] with non-zero mean : 3790.654
## ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : 3790.724
## ARIMA(2,0,1)(2,0,2)[12] with non-zero mean : 3781.735
## ARIMA(2,0,1)(1,0,2)[12] with non-zero mean : 3788.696
## ARIMA(2,0,1)(2,0,1)[12] with non-zero mean : 3784.409
## ARIMA(2,0,1)(1,0,1)[12] with non-zero mean : 3789.534
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : 3788.727
## ARIMA(2,0,0)(2,0,2)[12] with non-zero mean : 3789.039
## ARIMA(3,0,1)(2,0,2)[12] with non-zero mean : 3789.002
## ARIMA(1,0,0)(2,0,2)[12] with non-zero mean : 3788.239
## ARIMA(3,0,0)(2,0,2)[12] with non-zero mean : 3790.836
## ARIMA(3,0,2)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(2,0,1)(2,0,2)[12] with zero mean : 3808.891
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(2,0,1)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(2,0,1)(2,0,1)[12] with non-zero mean : 3807.323
##
## Best model: ARIMA(2,0,1)(2,0,1)[12] with non-zero mean## Series: arc
## ARIMA(2,0,1)(2,0,1)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 ma1 sar1 sar2 sma1 mean
## -0.1147 0.5533 0.9410 0.7711 -0.1984 -0.3662 2106.3192
## s.e. 0.0717 0.0697 0.0394 0.3031 0.1273 0.2983 136.2093
##
## sigma^2 estimated as 191849: log likelihood=-1895.66
## AIC=3807.32 AICc=3807.91 BIC=3835.59pred_sal6 <- forecast(ammodel6, level =c (95), h= 3*12)
plot(pred_sal6,main = "American Indian Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")
pred_sal6[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 2018 1702.926 1882.618 1663.507 1735.157 1717.477 1892.051
## 2019 2020.267 1939.082 2042.279 1970.998 2002.158 2000.151 2049.716
## 2020 2088.162 2059.132 2100.194 2090.905 2098.877 2102.274 2104.682
## 2021 2109.250
## Aug Sep Oct Nov Dec
## 2018 1869.938 1880.354 2261.006 2293.533 2209.224
## 2019 2030.346 2059.284 2137.659 2148.381 2129.658
## 2020 2095.063 2114.549 2100.083 2101.409 2104.084
## 2021forecasting for race 4: Asian Alone
library(forecast)
library(readr)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
asracefullrows <- subset(newracefullrows,race == "Asian Alone",select=c(year,EarnS,race))
head(asracefullrows)asracefullrows$year <- paste(asracefullrows$year,"/01/01",sep ="")
asrc <- ts(asracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns7 <- decompose(asrc)
plot(ns7)
plot(ns7$seasonal)
plot(ns7$trend)
plot(asracefullrows$EarnS,type ="l",main = "Asian Alone monthly salary",xlab= "Year",ylab="monthly Salary")
class(ns7)## [1] "decomposed.ts"ammodel7 <- auto.arima(asrc)
ammodel7## Series: asrc
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 sar1 sar2 sma1 sma2 mean
## 0.5233 0.2778 0.8458 -0.6814 -0.4876 0.7440 2353.9891
## s.e. 0.0811 0.0937 0.2339 0.1356 0.2291 0.0859 139.7658
##
## sigma^2 estimated as 319425: log likelihood=-1964.64
## AIC=3945.29 AICc=3945.88 BIC=3973.56auto.arima(asrc,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(0,0,0) with non-zero mean : 4115.845
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3960.971
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3991.858
## ARIMA(0,0,0) with zero mean : 4682.466
## ARIMA(1,0,0) with non-zero mean : 4010.36
## ARIMA(1,0,0)(2,0,0)[12] with non-zero mean : 3960.842
## ARIMA(1,0,0)(2,0,1)[12] with non-zero mean : 3959.003
## ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : 3961.816
## ARIMA(1,0,0)(2,0,2)[12] with non-zero mean : 3953.41
## ARIMA(1,0,0)(1,0,2)[12] with non-zero mean : 3957.728
## ARIMA(0,0,0)(2,0,2)[12] with non-zero mean : 4087.26
## ARIMA(2,0,0)(2,0,2)[12] with non-zero mean : 3950.507
## ARIMA(2,0,0)(1,0,2)[12] with non-zero mean : 3951.366
## ARIMA(2,0,0)(2,0,1)[12] with non-zero mean : 3955.018
## ARIMA(2,0,0)(1,0,1)[12] with non-zero mean : 3958.506
## ARIMA(3,0,0)(2,0,2)[12] with non-zero mean : 3953.291
## ARIMA(2,0,1)(2,0,2)[12] with non-zero mean : 3951.846
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : 3948.98
## ARIMA(1,0,1)(1,0,2)[12] with non-zero mean : 3950.233
## ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 3954.113
## ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 3957.247
## ARIMA(0,0,1)(2,0,2)[12] with non-zero mean : 3974.776
## ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : 3950.933
## ARIMA(0,0,2)(2,0,2)[12] with non-zero mean : 3954.604
## ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 3953.824
## ARIMA(1,0,1)(2,0,2)[12] with zero mean : 3986.575
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : 3945.288
##
## Best model: ARIMA(1,0,1)(2,0,2)[12] with non-zero mean## Series: asrc
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 sar1 sar2 sma1 sma2 mean
## 0.5233 0.2778 0.8458 -0.6814 -0.4876 0.7440 2353.9891
## s.e. 0.0811 0.0937 0.2339 0.1356 0.2291 0.0859 139.7658
##
## sigma^2 estimated as 319425: log likelihood=-1964.64
## AIC=3945.29 AICc=3945.88 BIC=3973.56pred_sal7 <- forecast(ammodel7, level =c (95), h= 3*12)
plot(pred_sal7,main = "Asian Alone Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")
pred_sal7[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 2018 2246.788 1990.303 1688.875 1641.645 1499.334 2408.398
## 2019 1626.769 1575.951 1864.390 1913.247 1894.348 2002.206 2422.265
## 2020 2103.070 2050.241 2334.909 2511.466 2490.940 2659.929 2385.707
## 2021 2637.532
## Aug Sep Oct Nov Dec
## 2018 2409.603 2049.515 2349.585 2354.384 2123.790
## 2019 2415.149 2614.538 1867.633 1865.128 2162.898
## 2020 2373.603 2784.879 1947.189 1941.045 2349.654
## 2021forecasting for race 5: Native Hawaiian or Other Pacific Islander Alone
library(forecast)
library(readr)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
nracefullrows <- subset(newracefullrows,race == "Native Hawaiian or Other Pacific Islander Alone",select=c(year,EarnS,race))
head(nracefullrows)nracefullrows$year <- paste(nracefullrows$year,"/01/01",sep ="")
nrc <- ts(nracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns8 <- decompose(nrc)
plot(ns8)
plot(ns8$seasonal)
plot(ns8$trend)
plot(nracefullrows$EarnS,type ="l",main = "Native Hawaiian or Other Pacific Islander Alone monthly salary",xlab= "Year",ylab="monthly Salary")
class(ns8)## [1] "decomposed.ts"ammodel8 <- auto.arima(nrc)
ammodel8## Series: nrc
## ARIMA(2,0,3)(2,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 ma1 ma2 ma3 sar1 sar2 mean
## -0.6083 -0.4481 1.4826 1.4596 0.7730 0.1992 0.1453 1925.2832
## s.e. 0.0775 0.0876 0.0562 0.0661 0.0488 0.0738 0.0715 79.4667
##
## sigma^2 estimated as 144958: log likelihood=-1860.42
## AIC=3738.83 AICc=3739.57 BIC=3770.63auto.arima(nrc,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3742.029
## ARIMA(0,0,0) with non-zero mean : 3923.893
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3750.42
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3792.06
## ARIMA(0,0,0) with zero mean : 4568.682
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 3760.312
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 3740.043
## ARIMA(2,0,2) with non-zero mean : 3773.233
## ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 3741.812
## ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 3743.027
## ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 3744.166
## ARIMA(2,0,1)(1,0,0)[12] with non-zero mean : 3744.08
## ARIMA(3,0,2)(1,0,0)[12] with non-zero mean : 3743.141
## ARIMA(2,0,3)(1,0,0)[12] with non-zero mean : 3720.216
## ARIMA(2,0,3) with non-zero mean : 3757.906
## ARIMA(2,0,3)(2,0,0)[12] with non-zero mean : 3717.277
## ARIMA(2,0,3)(2,0,1)[12] with non-zero mean : 3718.794
## ARIMA(2,0,3)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(1,0,3)(2,0,0)[12] with non-zero mean : 3722.853
## ARIMA(3,0,3)(2,0,0)[12] with non-zero mean : 3733.996
## ARIMA(2,0,4)(2,0,0)[12] with non-zero mean : 3726.845
## ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 3745.564
## ARIMA(1,0,4)(2,0,0)[12] with non-zero mean : 3723.353
## ARIMA(3,0,2)(2,0,0)[12] with non-zero mean : 3740.366
## ARIMA(3,0,4)(2,0,0)[12] with non-zero mean : Inf
## ARIMA(2,0,3)(2,0,0)[12] with zero mean : Inf
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(2,0,3)(2,0,0)[12] with non-zero mean : 3738.831
##
## Best model: ARIMA(2,0,3)(2,0,0)[12] with non-zero mean## Series: nrc
## ARIMA(2,0,3)(2,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 ma1 ma2 ma3 sar1 sar2 mean
## -0.6083 -0.4481 1.4826 1.4596 0.7730 0.1992 0.1453 1925.2832
## s.e. 0.0775 0.0876 0.0562 0.0661 0.0488 0.0738 0.0715 79.4667
##
## sigma^2 estimated as 144958: log likelihood=-1860.42
## AIC=3738.83 AICc=3739.57 BIC=3770.63pred_sal8 <- forecast(ammodel8, level =c (95), h= 3*12)
plot(pred_sal8,main = "Native Hawaiian or Other Pacific Islander Alone Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")
pred_sal8[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 2018 1166.068 1936.361 1954.665 1755.968 2112.173 2125.093
## 2019 1660.343 1657.440 1883.304 1893.981 1830.489 2076.199 2087.439
## 2020 1739.209 1761.587 1918.528 1923.324 1881.794 1982.502 1986.621
## 2021 1849.717
## Aug Sep Oct Nov Dec
## 2018 2034.552 1828.888 1852.484 1771.206 1668.979
## 2019 2017.282 1870.838 1885.504 1834.802 1754.709
## 2020 1959.487 1900.429 1906.780 1884.868 1854.058
## 2021forecasting for race 6: Two or More Race Groups
library(forecast)
library(readr)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
tracefullrows <- subset(newracefullrows,race == "Two or More Race Groups",select=c(year,EarnS,race))
head(tracefullrows)tracefullrows$year <- paste(tracefullrows$year,"/01/01",sep ="")
trc <- ts(tracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns9 <- decompose(trc)
plot(ns9)
plot(ns9$seasonal)
plot(ns9$trend)
plot(tracefullrows$EarnS,type ="l",main = "Two or More Race Groups",xlab= "Year",ylab="monthly Salary")
class(ns9)## [1] "decomposed.ts"ammodel9 <- auto.arima(trc)
ammodel9## Series: trc
## ARIMA(1,0,0)(1,0,1)[12] with non-zero mean
##
## Coefficients:
## ar1 sar1 sma1 mean
## 0.6582 -0.2081 0.562 2121.6605
## s.e. 0.0474 0.1509 0.124 104.7002
##
## sigma^2 estimated as 203352: log likelihood=-1904.48
## AIC=3818.97 AICc=3819.21 BIC=3836.63auto.arima(trc,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3796.14
## ARIMA(0,0,0) with non-zero mean : 3973.154
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3801.375
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3856.026
## ARIMA(0,0,0) with zero mean : 4617.079
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 3827.078
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 3805.261
## ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 3797.924
## ARIMA(2,0,2)(1,0,2)[12] with non-zero mean : 3798.051
## ARIMA(2,0,2) with non-zero mean : Inf
## ARIMA(2,0,2)(0,0,2)[12] with non-zero mean : 3828.083
## ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 3796.616
## ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 3799.845
## ARIMA(1,0,2)(1,0,1)[12] with non-zero mean : 3793.098
## ARIMA(1,0,2)(0,0,1)[12] with non-zero mean : 3824.137
## ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 3802.372
## ARIMA(1,0,2)(2,0,1)[12] with non-zero mean : 3794.94
## ARIMA(1,0,2)(1,0,2)[12] with non-zero mean : 3795
## ARIMA(1,0,2) with non-zero mean : 3844.543
## ARIMA(1,0,2)(0,0,2)[12] with non-zero mean : 3825.075
## ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 3793.623
## ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : 3796.867
## ARIMA(0,0,2)(1,0,1)[12] with non-zero mean : 3799.922
## ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 3791.436
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 3822.663
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 3801.19
## ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 3793.187
## ARIMA(1,0,1)(1,0,2)[12] with non-zero mean : 3793.319
## ARIMA(1,0,1) with non-zero mean : 3842.545
## ARIMA(1,0,1)(0,0,2)[12] with non-zero mean : 3823.38
## ARIMA(1,0,1)(2,0,0)[12] with non-zero mean : 3791.915
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : 3795.131
## ARIMA(0,0,1)(1,0,1)[12] with non-zero mean : 3827.723
## ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : 3791.282
## ARIMA(1,0,0)(0,0,1)[12] with non-zero mean : 3822.746
## ARIMA(1,0,0)(2,0,1)[12] with non-zero mean : 3792.581
## ARIMA(1,0,0)(1,0,2)[12] with non-zero mean : 3793.18
## ARIMA(1,0,0) with non-zero mean : 3842.186
## ARIMA(1,0,0)(0,0,2)[12] with non-zero mean : 3823.455
## ARIMA(1,0,0)(2,0,0)[12] with non-zero mean : 3791.389
## ARIMA(1,0,0)(2,0,2)[12] with non-zero mean : 3794.557
## ARIMA(0,0,0)(1,0,1)[12] with non-zero mean : 3937.966
## ARIMA(2,0,0)(1,0,1)[12] with non-zero mean : 3792.297
## ARIMA(2,0,1)(1,0,1)[12] with non-zero mean : 3793.644
## ARIMA(1,0,0)(1,0,1)[12] with zero mean : 3829.254
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : 3818.966
##
## Best model: ARIMA(1,0,0)(1,0,1)[12] with non-zero mean## Series: trc
## ARIMA(1,0,0)(1,0,1)[12] with non-zero mean
##
## Coefficients:
## ar1 sar1 sma1 mean
## 0.6582 -0.2081 0.562 2121.6605
## s.e. 0.0474 0.1509 0.124 104.7002
##
## sigma^2 estimated as 203352: log likelihood=-1904.48
## AIC=3818.97 AICc=3819.21 BIC=3836.63pred_sal9 <- forecast(ammodel9, level =c (95), h= 3*12)
plot(pred_sal9,main = "Two or More Race Groups Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")
pred_sal9[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 2018 1908.027 1905.768 1951.572 1939.979 1964.337 1988.491
## 2019 2170.082 2168.035 2167.846 2157.883 2160.009 2154.754 2149.605
## 2020 2111.606 2112.025 2112.060 2114.130 2113.686 2114.778 2115.848
## 2021 2123.753
## Aug Sep Oct Nov Dec
## 2018 1997.149 1972.695 2342.373 2360.073 2269.647
## 2019 2147.722 2152.756 2075.809 2072.103 2090.901
## 2020 2116.239 2115.192 2131.200 2131.971 2128.060
## 2021ggplot(data=newsalfullrows,aes(x= factor(year),y= mon_wage,fill= sex))+geom_bar(stat="identity",position= "dodge")+labs(title="Monthly salary of both male and female",x="year",y="Monthly salary")
ggplot(data=newsalfullrows,aes(x=EmpTotal,y=mon_wage,color=sex))+geom_point()+geom_smooth(color="black")+labs(title="Monthly salary vs No of employees",x="Monthly salary", y="No of Employees")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ggplot(data=newracefullrows,aes(x= year,y= EarnS,color= race))+geom_point()+geom_smooth(color="black")+scale_y_continuous(limit=c(0,20000))## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
ggplot(data=newracefullrows,aes(x= year,y= EarnS))+geom_bar(stat="identity",position="dodge",aes(fill= race))+scale_y_continuous(limit=c(0,20000))## Warning: Removed 20 rows containing missing values (geom_bar).
subsetting male average salary
library('ggplot2')
library('forecast')
library('tseries')
#newsal <- drop_na(Sal)
newsalfullrows$year <- as.Date(newsalfullrows$year)
nsal <- subset(newsalfullrows, sex == "Male", select = c(mon_wage, year,sex,industry))
head(nsal)dd <- aggregate(nsal$mon_wage ,by=list(dept = nsal$industry), FUN = mean)
head(dd)sorted <- dd[order(-dd$x),]
topSorted <- sorted[c(1:3),]
#plot : Males who earn highest in top departemnts
ggplot(data =topSorted, aes(x= dept, y= x) )+geom_bar(stat = "identity",fill = "blue")+labs(x = "Department", y = "salary for Male",title = "Males who earn highest in top departemnts")
# Bottom 3 departments who earn lowest
bsorted <- dd[order(dd$x),]
botSorted <- bsorted[c(1:3),]
#plot : Males who earn lowest in bottom departemnts
ggplot(data =botSorted, aes(x= dept, y= x) )+geom_bar(stat = "identity",fill = "dark green")+labs(x = "Department", y = "salary for Male",title = "Males who earn lowest in bottom departemnts")+theme(axis.text.x = element_text(angle=45,hjust = 1))
#subsetting female average salary
fnsal <- subset(newsalfullrows, sex == "Female", select = c(mon_wage, year,industry,sex))
head(fnsal)dd1 <- aggregate(fnsal$mon_wage ,by=list(dept = fnsal$industry), FUN = mean)
head(dd1)sorted1 <- dd1[order(-dd1$x),]
topSorted1 <- sorted1[c(1:3),]
#plot : Females who earn highest in top departemnts
ggplot(data =topSorted1, aes(x= dept, y= x) )+geom_bar(stat = "identity",fill = "pink")+ labs(x = "Department", y = "salary for Female",title = " Females who earn highest in top departemnts")
# Bottom 3 departments who earn lowest for female
bfsorted <- dd1[order(dd1$x),]
botfSorted <- bfsorted[c(1:3),]
#plot : Males who earn lowest in bottom departemnts
ggplot(data =botfSorted, aes(x= dept, y= x) )+geom_bar(stat = "identity",fill = "violet")+labs(x = "Department", y = "salary for Female",title = "females who earn lowest in bottom departemnts")+theme(axis.text.x = element_text(angle=45,hjust = 1))
#Subsetting for male and female who earn highest in top 8 departments
nsal1 <- subset(newsalfullrows, sex == "Male" | sex == "Female", select = c(mon_wage, year,industry,sex))
head(nsal1)dd2 <- aggregate(nsal1$mon_wage ,by=list(dept = nsal1$industry,sex= nsal1$sex), FUN = mean)
head(dd2)sorted2 <- dd2[order(-dd2$x),]
topSorted2 <- sorted2[c(1:10),]
ggplot(data =topSorted2, aes(x= dept, y= x,fill= sex) )+geom_bar(stat = "identity",position ="dodge")+labs(x = "Department", y = "Average salary",title = "Males and Females who earn highest in top 8 departemnts ")+theme(axis.text.x = element_text(angle = 45,hjust=1))
sorted3 <- dd2[order(dd2$x),]
bSorted3 <- sorted3[c(1:10),]
ggplot(data =bSorted3, aes(x= dept, y= x,fill= sex) )+geom_bar(stat = "identity",position ="dodge")+labs(x = "Department", y = "Average salary",title = "Males and Females who earn lowest in top 8 departemnts ")+theme(axis.text.x = element_text(angle = 45,hjust=1))
newsal <- drop_na(Sal)loading average salary male and females:dataset 2(Average salary)
library(forecast)
library(readr)
newsal$avg_wage_ft <- as.numeric(newsal$avg_wage_ft)
library(zoo)
newsal$Date <- paste (newsal$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata1 <- ts(newsal$avg_wage_ft,frequency = 12)
tsdata1## Jan Feb Mar Apr May Jun Jul
## 1 55458.50 86682.40 54431.80 85707.40 55109.50 85267.30 18393.40
## 2 99920.40 178424.00 127523.00 175534.00 128534.00 177805.00 31223.40
## 3 58112.50 66271.40 56859.20 70643.50 56820.50 74757.40 60364.50
## 4 53526.10 63128.90 54962.60 59704.90 55028.40 63631.80 83664.60
## 5 43737.20 70865.60 44096.90 57713.90 46257.90 57285.50 33057.80
## 6 42522.10 65784.40 45328.70 70146.20 46605.20 72558.50 33219.20
## 7 79105.70 81777.00 73835.60 84362.90 75047.70 84184.80 40489.90
## 8 53670.20 114071.00 54758.00 117501.00 71981.80 121559.00 39549.50
## 9 21655.00 35993.20 20995.30 30974.90 24421.60 56441.90 31216.40
## 10 27280.20 43099.00 30139.00 35946.40 31073.30 43311.30 31448.70
## 11 48093.40 62677.00 47688.70 64969.30 49037.10 66329.80 52731.10
## 12 108388.00 115709.00 109712.00 120292.00 105819.00 122867.00 45350.90
## 13 39494.80 58937.00 36048.70 60610.60 32984.60 55121.80 66644.30
## 14 40377.10 68698.10 37365.50 68460.00 37426.70 73682.90 68732.10
## 15 64332.90 73956.70 75341.10 79193.40 74997.10 104016.00 22098.40
## 16 27475.10 37987.60 27695.60 39227.20 28458.80 40428.60 31610.70
## 17 35086.40 35455.50 45162.80 34173.20 44099.10 36675.70 40037.90
## 18 28303.80 28284.00 29367.60 40103.30 43058.00 39944.20 42790.00
## 19 28776.30 24135.80 27830.30 18904.00 26104.10 20467.70 25778.40
## 20 32814.80 28655.30 34589.20 33819.20 34664.70 34310.50 34418.10
## 21 47352.10 32984.30 46781.80 53531.90 57650.40 52171.90 55980.80
## 22 48361.30 42067.60 45596.10 82364.60 117369.00 81726.20 198142.00
## 23 36745.80 41292.60 36745.20 42417.60 37645.00 43499.10 31244.30
## 24 48312.50 45628.70 48296.30 45393.80 48689.80 32468.70 52017.90
## 25 73451.00 74795.70 79621.50 67236.50 73783.00 28905.00 40348.10
## 26 31949.10 26452.10 30396.10 26328.70 30816.10 47082.80 72404.30
## 27 31178.10 23831.40 30338.30 24716.00 31498.20 37122.20 77211.20
## 28 51424.40 20025.30 31872.40 19912.70 33266.50 33303.60 35392.40
## 29 32947.30 31594.00 31612.00 30774.80 32333.10 20562.00 29171.10
## 30 27833.70 21384.80 28314.80 21870.20 28807.70 30289.30 31249.60
## 31 34437.90 28188.00 34740.20 30208.60 34290.90 79828.50 103558.00
## 32 46751.60 23489.80 47027.00 27868.60 46231.00 47186.70 49411.20
## 33 71998.50 58387.50 69198.40 62070.00 69581.20 105175.00 147386.00
## 34 22086.00 19992.10 21086.80 20871.90 21237.60 50021.20 95376.10
## 35 90281.20 69943.50 89507.80 71937.80 93124.60 50403.00 57772.80
## 36 25126.90 21922.60 24283.20 21552.70 25006.70 40946.90 50389.30
## 37 53101.60 46479.80 50188.00 46874.10 52099.30 21996.70 30748.90
## 38 20940.80 19315.40 22047.40 19958.50 22863.50 59180.90 80767.10
## 39 109032.00 71827.30 104883.00 71994.90 105495.00 46794.00 62873.40
## 40 72414.50 56704.30 70993.60 58544.10 67459.30 54070.70 90825.90
## 41 98374.30 88448.00 100899.00 86841.20 103552.00 39206.40 42945.60
## 42 78799.30 60543.50 76532.70 66690.70 81124.70 83858.90 86707.50
## 43 42615.00 33239.50 42966.10 34427.80 42349.40 68039.40 78912.60
## 44 53489.30 48602.60 53279.70 51058.20 53781.80 68051.40 80560.80
## 45 43224.20 38836.20 43356.20 36705.70 43244.00 47365.60 70464.30
## 46 54163.90 40154.90 52371.40 39674.30 53941.70 27985.80 36532.00
## 47 28939.00 29296.40 29854.10 29635.60 33108.80 64519.40 74143.00
## 48 46767.50 45848.90 46138.00 41185.20 46418.60 35397.80 39053.40
## 49 22975.20 20753.90 22900.50 21132.00 23543.40 29929.10 34313.30
## 50 67338.40 41775.90 66080.70 49642.30 63924.40 41559.30 42698.80
## 51 38262.10 31284.10 35025.50 31681.70 39610.50 13036.90 17206.60
## 52 42302.20 28110.70 41039.40 31344.10 39008.10 33183.40 51499.00
## 53 48498.60 48604.00 63359.80 66689.30 60697.00 67387.50 58633.50
## 54 41969.20 67369.30 56849.20 74121.90 53925.50 77619.80 57143.10
## 55 20057.10 23820.50 27276.50 30990.90 27416.80 31718.40 28150.10
## 56 30522.90 37069.30 24578.60 25750.50 24211.20 26901.10 20018.80
## 57 26253.10 64308.40 30481.80 32571.00 29974.60 34649.50 30044.90
## 58 68362.70 83640.60 29634.10 41768.90 30393.20 41615.20 30670.50
## 59 52128.30 74604.80 145786.00 166196.00 153984.00 181351.00 157053.00
## 60 57316.40 45949.00 57447.30 45763.60 56718.50 58566.70 73800.00
## 61 55437.20 49924.20 53795.50 50093.10 58516.00 40905.50 47055.80
## 62 41974.20 39524.30 41757.60 37177.00 47655.60 14482.40 17772.50
## 63 37704.50 32520.70 37518.10 32828.80 39116.90 24027.70 33005.80
## 64 47082.30 38483.70 47294.80 39766.30 48116.60 40090.40 42263.30
## 65 38292.40 31577.30 38937.50 19806.60 24943.20 18501.80 25056.90
## 66 42113.70 11245.80 48268.80 78031.20 79033.30 64595.20 98212.30
## 67 49621.80 47054.30 48605.60 59811.90 81918.30 59807.80 79195.10
## 68 41058.50 29181.80 42959.10 82476.30 90671.60 83972.40 89170.80
## 69 51334.00 44163.60 48570.60 27575.80 58145.50 27722.20 58681.00
## 70 26530.60 26093.60 26230.40 35365.50 40313.60 25923.30 39379.80
## 71 26470.20 22839.70 24371.50 30600.90 38832.00 26359.70 38817.70
## 72 46796.30 43366.10 46241.10 71574.80 85748.90 50835.30 73100.40
## 73 50174.70 37505.10 52365.10 27644.00 33733.50 28776.20 32303.80
## 74 59945.50 42538.50 54194.90 42442.70 50799.70 42512.50 49596.70
## 75 28212.50 39203.80 44065.80 57192.40 43773.70 57424.50 44747.40
## 76 32332.50 41947.70 64518.90 72987.60 62123.40 72236.70 62268.50
## 77 49982.70 68058.00 25729.40 35305.30 31905.50 34485.90 30293.60
## 78 41465.30 42585.70 18837.30 35529.80 18307.40 31803.80 18242.10
## 79 26768.80 29666.00 54454.00 60794.50 57619.60 60848.80 52386.20
## 80 31660.20 34355.90 31367.40 32555.60 30636.90 31251.20 64880.00
## 81 54697.70 169056.00 51167.50 162353.00 54359.70 186121.00 65889.90
## 82 57784.00 69936.90 63806.10 69457.40 67449.90 68128.30 66083.00
## 83 66211.20 67142.20 62673.80 65911.90 61867.60 66242.30 51786.70
## 84 53013.20 53132.30 52889.40 53128.60 51918.80 57329.20 26136.10
## 85 90773.00 92230.70 77126.70 87517.40 74819.70 88172.00 27225.70
## 86 53675.70 63776.20 54251.90 63217.30 62297.70 65117.60 30190.70
## 87 37238.70 37257.90 36209.70 39369.60 37008.70 40531.60 51983.90
## 88 58332.70 79554.70 57048.90 81774.20 59867.40 83199.10 43367.20
## 89 38387.30 54065.90 37200.80 50821.20 36492.30 51803.10 65271.00
## 90 47183.40 59112.60 45294.90 59724.30 40453.00 58455.30 46268.70
## 91 38397.30 56839.40 38660.00 55665.90 39234.30 55471.40 20321.20
## 92 41736.90 54521.70 43956.70 55223.60 46272.70 56766.00 29103.40
## 93 26007.00 27846.00 26253.50 26280.20 25795.00 27351.20 17042.60
## 94 19040.30 19339.10 18842.30 19964.20 18939.60 19860.10 28827.40
## 95 21310.20 25829.10 20543.70 26847.90 21533.00 28193.80 38503.10
## 96 12645.00 31276.20 11841.20 31908.50 11962.40 31894.00 34830.10
## 97 71721.70 55342.50 69822.40 25930.80 39988.10 32272.30 33655.50
## 98 44579.60 21120.60 23179.20 27416.50 25031.60 27959.90 25347.90
## 99 49366.60 99102.10 45600.20 47039.10 42968.60 45448.30 40492.00
## 100 66642.50 96809.40 46683.60 78752.80 53889.30 67762.30 61653.30
## 101 30640.70 14648.40 23184.30 15195.00 21893.80 15228.60 24262.40
## 102 25323.00 24914.10 38987.10 25246.60 32569.10 25257.80 33331.30
## 103 33769.50 32220.40 44747.90 32220.10 49387.40 45623.00 48682.70
## 104 44294.50 24919.40 36086.30 25367.50 36551.70 25180.40 35938.10
## 105 42869.50 11059.60 42318.10 11072.30 43090.60 11212.20 44611.20
## 106 24146.50 21338.00 25052.40 20843.00 21211.70 21109.00 21782.20
## 107 22804.30 31694.70 34176.10 31250.20 34742.00 31553.50 32064.20
## 108 36511.00 18819.20 22856.10 17686.20 20585.60 17273.50 20831.50
## 109 22104.10 33151.40 45515.70 33938.50 49016.40 38018.60 49691.60
## 110 81530.90 53471.80 64026.20 53608.90 64505.00 54765.30 67854.70
## 111 45688.70 57046.00 74577.40 53723.70 73078.80 65013.40 74917.30
## 112 70774.20 30047.20 32928.00 30276.70 32840.30 31325.20 33164.20
## 113 43037.70 65044.00 79669.50 65894.00 91059.80 74213.20 96155.50
## 114 47446.70 30521.20 30678.20 31594.80 32521.10 29577.70 31214.70
## 115 44340.80 50347.80 78185.10 49188.90 75587.30 48641.80 76563.50
## 116 70280.40 29988.20 30031.90 31265.30 32453.30 31616.40 37544.50
## 117 26765.50 37854.20 38563.20 35604.70 37665.80 35665.80 41866.80
## 118 84098.70 22926.00 28134.50 23363.00 28860.60 22743.40 29274.40
## 119 31221.20 102656.00 153323.00 103134.00 155950.00 107421.00 157104.00
## 120 50641.50 24906.50 30015.50 24255.60 26714.90 25049.00 28766.20
## 121 38624.00 40209.10 45885.30 39084.60 45522.20 38954.80 44898.30
## 122 28482.00 38859.10 59762.90 38269.20 57690.60 40761.10 51806.20
## 123 40619.60 47592.00 64225.50 46210.10 61959.60 46588.00 76301.40
## 124 65700.20 39542.90 10012.60 37354.70 10139.20 38919.20 40410.30
## 125 21068.90 38234.00 18595.70 28854.50 19171.30 29221.80 24029.20
## 126 24326.40 40927.20 23842.90 40884.50 27172.40 43870.00 19319.90
## 127 25548.10 32545.30 26980.90 33030.30 27590.40 33880.70 34697.00
## 128 38993.60 43946.30 36626.60 43996.90 38812.30 69354.70 93881.10
## 129 91288.90 89259.90 105870.00 84971.80 92833.80 56030.80 77179.00
## 130 103208.00 69231.70 104354.00 71384.60 103835.00 28728.10 36401.80
## 131 115731.00 91785.80 97187.20 76672.80 93234.30 59849.00 74851.40
## 132 104462.00 70796.10 101133.00 71221.40 99247.20 27118.70 41110.60
## 133 45423.20 35841.00 43039.20 36879.40 41204.90 64251.40 101621.00
## 134 33218.60 19433.60 31268.60 32308.20 41121.20 32582.30 40054.00
## 135 52041.60 42973.60 51712.60 43950.50 59460.10 31558.20 27825.00
## 136 35915.40 39567.50 19143.70 28745.10 23097.40 30590.40 24249.20
## 137 61215.70 83952.80 46022.90 47326.70 43451.10 47874.90 44346.90
## 138 15210.90 56131.20 35222.80 64311.70 37073.30 129541.00 41991.60
## 139 16787.90 21062.50 23806.50 31014.20 24378.70 29276.40 24877.40
## 140 43037.80 55409.90 25475.30 30657.20 26835.80 32449.40 26563.60
## 141 84283.80 92511.00 25873.70 26292.30 26624.00 26792.90 23814.00
## 142 49830.20 109827.00 18816.30 20867.50 19663.70 20181.90 19730.20
## 143 42494.10 61425.30 31979.10 36704.20 34270.80 36022.70 34602.70
## 144 40704.40 44883.90 45434.70 63497.80 45642.70 64617.30 45586.40
## 145 43935.80 59168.10 65138.90 91635.20 66296.50 91495.50 67523.00
## 146 24180.00 36636.20 71072.40 80534.10 65314.90 74850.20 64494.40
## 147 34941.90 43462.90 23796.50 33617.30 22617.60 31877.40 21524.70
## 148 36235.20 44185.30 20882.30 22929.10 21786.30 22955.90 22704.30
## 149 31036.10 46920.40 23592.40 34435.20 25132.30 34004.50 25466.30
## 150 53880.40 71518.10 19060.30 23382.60 19884.80 24793.60 19480.00
## 151 35150.60 40242.60 19964.40 28472.20 18098.20 29621.10 20473.70
## 152 28301.80 40660.90 36524.70 39003.80 35615.70 39860.30 13369.50
## 153 31211.30 54121.50 23240.80 30421.30 17214.50 28029.20 11430.40
## 154 43438.60 54219.60 25140.20 29193.90 20191.70 26301.90 20446.80
## 155 15148.30 26360.50 38533.50 44992.70 33103.50 49459.80 33018.60
## 156 90391.60 91465.80 60592.20 66088.00 61850.60 66200.80 65731.90
## 157 38211.30 56186.40 54529.60 68698.50 68612.10 68846.80 74978.00
## 158 25754.90 26502.80 116304.00 186196.00 116161.00 176829.00 115829.00
## 159 92550.60 122741.00 27215.50 36492.90 26348.50 36172.70 26109.90
## 160 65149.90 72910.40 34899.90 52043.90 30870.30 47376.70 48756.60
## 161 31432.20 35915.50 30929.00 31951.00 30930.70 31230.30 31459.20
## 162 60918.40 77105.30 42967.90 51009.40 43760.20 49866.90 42158.40
## 163 88307.20 123099.00 52846.40 65982.40 56571.80 66450.10 56698.90
## 164 48650.40 51949.50 43571.10 72590.90 41979.60 70595.50 42307.60
## 165 57247.60 90578.60 41945.00 59398.40 46451.90 61327.30 53564.80
## 166 61420.50 79300.90 43932.00 65670.70 43357.10 66565.20 45490.60
## 167 40443.70 52277.40 46911.60 58631.90 45984.10 53169.20 49532.10
## 168 23059.60 35650.80 19334.30 21187.40 20872.40 21998.00 20987.90
## 169 28446.00 35674.30 20755.20 36489.20 22504.50 38251.90 22767.10
## 170 31914.70 42461.20 46733.50 73812.80 47007.40 69092.80 46877.40
## 171 21408.60 33405.20 26332.70 40163.70 25787.40 39559.10 29351.40
## 172 10223.40 28491.20 38373.70 46889.90 37999.60 48855.70 38317.80
## 173 23108.70 23476.50 24017.50 30084.70 24225.70 28021.10 24241.90
## 174 80247.40 139495.00 36413.90 37481.80 30981.80 35857.50 32819.30
## 175 124580.00 124726.00 98192.20 118203.00 96341.90 116035.00 98169.70
## 176 31503.80 32136.70 28506.40 39295.70 28944.50 33565.40 29575.10
## 177 30074.40 45581.90 23175.20 32427.90 25156.10 36552.40 28633.30
## 178 45739.20 47819.70 65298.20 83125.70 64321.00 78467.90 66105.20
## 179 73786.60 107782.00 159392.00 229096.00 162641.00 233337.00 165391.00
## 180 36043.30 37348.30 27210.20 41738.20 27138.10 40317.00 30624.90
## 181 87999.50 88551.30 51625.50 60370.80 52904.50 60370.00 53600.10
## 182 53449.80 54972.40 49772.90 57133.60 49603.80 55722.10 50585.70
## 183 48528.40 52961.20 57749.50 78722.70 57601.80 77208.00 57953.70
## 184 31894.90 85486.20 39546.70 50765.00 36751.30 52479.30 36309.40
## 185 25685.40 37028.60 19187.00 23639.90 20829.70 23872.50 23939.00
## 186 18480.90 27293.90 21967.30 33334.90 22415.50 32688.30 23524.00
## 187 22585.50 33117.80 47434.80 63222.60 49620.90 62588.50 47950.30
## 188 39376.30 41663.20 61966.20 62445.80 60724.50 65856.30 64098.00
## 189 62831.00 67480.70 37115.20 56995.30 36939.00 57334.80 38090.30
## 190 32608.80 35804.70 49455.20 78612.70 49745.70 79930.90 50495.30
## 191 60540.80 95234.00 63065.10 95693.80 77160.00 98623.80 74678.80
## 192 61742.80 95578.30 26323.50 70485.30 25911.80 50791.40 30745.40
## 193 47564.90 55830.10 65232.60 91862.10 62492.90 96020.30 63073.60
## 194 72656.80 78735.20 48270.10 50813.40 43430.30 49845.40 50231.10
## 195 54692.90 65632.70 20773.60 34663.90 20797.50 39032.10 21060.20
## 196 57948.00 76526.20 25755.90 31560.90 25753.30 32816.10 25808.10
## 197 30298.10 38117.20 43350.40 119092.00 36886.30 55356.20 39175.90
## 198 26139.10 30686.50 61454.90 70718.10 60501.50 68353.40 60642.90
## 199 34005.20 39751.00 34404.40 56332.10 35392.40 56558.80 37129.20
## 200 19027.20 25651.80 52686.30 71470.50 52162.30 71054.80 53350.60
## 201 30758.80 44295.60 45572.90 46700.70 36016.70 26621.90 59686.80
## 202 36559.60 29237.90 42329.90 29358.10 39826.40 36867.80 52557.60
## 203 189149.00 91961.20 118336.00 99695.50 112827.00 49126.00 70888.10
## 204 77015.90 63910.70 77255.00 66644.70 79527.00 26417.60 27604.60
## 205 48520.70 45617.50 48194.00 46265.60 48187.70 35292.00 40494.50
## 206 92986.90 69643.00 92891.80 72220.40 102640.00 40517.60 47392.30
## 207 30964.80 29424.70 30144.50 29677.10 30058.60 20040.90 20890.00
## 208 35372.90 23297.70 36388.80 25503.60 37546.60 73089.20 76635.90
## 209 31404.90 27843.20 30397.90 27690.10 30310.70 8337.42 32737.80
## 210 33336.90 10075.90 35445.30 56913.10 81716.10 55689.10 78920.50
## 211 40196.50 38829.90 40323.10 42280.20 47064.40 42157.40 46718.30
## 212 84648.40 71368.60 86606.00 40384.20 43021.80 37094.80 41589.80
## 213 67482.10 60623.30 62392.30 44549.40 48823.60 40484.70 43587.90
## 214 47785.90 36676.30 47750.30 27514.90 27640.20 26849.70 26992.10
## 215 31813.00 20025.30 35351.20 20278.30 34929.50 35841.30 37961.80
## 216 51411.30 44249.80 53066.00 44316.00 51574.20 38018.60 40437.10
## 217 51848.60 47913.00 53466.00 54422.70 62237.30 28028.00 39850.60
## 218 29322.70 28720.00 32327.80 29191.10 29358.80 42897.90 73259.60
## 219 59420.40 37346.20 61474.40 36901.50 59139.90 24029.60 30658.40
## 220 31692.40 21498.60 31043.60 22504.20 31319.90 51975.20 62947.20
## 221 46056.40 42027.80 47831.90 41292.50 44274.20 24739.00 26811.10
## 222 34543.80 25808.60 36322.80 25231.50 37546.60 33252.40 41355.00
## 223 28740.70 27196.70 28144.90 27462.10 28366.40 26321.90 39789.20
## 224 61512.90 62290.20 2002.53 2027.83 49199.10 41721.80 105603.00
## 225 38502.30 27320.10 50883.30 26247.00 48650.90 66966.40 83414.50
## 226 57278.90 52892.70 56145.60 52604.60 59369.60 28917.50 31673.50
## 227 96525.50 56756.50 94786.60 66103.40 85413.40 35360.50 42773.80
## 228 56818.20 51525.70 55409.00 51108.00 51822.60 37084.80 43913.30
## 229 31509.90 25370.70 28937.80 28884.60 29424.50 65047.80 77743.00
## 230 20587.60 19530.00 20527.40 12707.00 24554.60 86300.00 131111.00
## 231 27128.60 23047.80 25399.10 23029.10 27583.10 21497.60 25065.70
## 232 51009.60 46019.80 46707.10 43031.20 47588.50 50820.70 64753.80
## 233 35983.70 27159.60 36495.40 28285.40 36839.20 22150.80 37276.80
## 234 51464.50 43028.50 52686.40 41019.40 50023.60 20480.70 24908.80
## 235 34129.40 33181.10 34563.40 34022.40 34160.10 46394.70 65888.30
## Aug Sep Oct Nov Dec
## 1 51929.70 18262.40 49598.00 17693.80 50787.10
## 2 48778.20 31693.80 33370.20 35615.50 36439.60
## 3 103035.00 56464.60 101524.00 58797.30 95358.60
## 4 92254.20 78976.00 91961.30 84087.90 90847.90
## 5 39245.60 40732.10 42177.00 37884.60 45607.10
## 6 46509.80 36176.50 46152.10 35251.20 49015.60
## 7 50066.80 28436.40 49178.10 25723.20 50804.80
## 8 65598.40 38154.10 43861.10 44481.30 45188.80
## 9 33578.50 31982.60 33891.00 30460.30 37167.80
## 10 84891.40 33105.90 74257.20 48879.90 78098.90
## 11 80998.70 50930.70 87303.10 54658.70 88056.70
## 12 57660.30 46966.60 60639.70 38996.70 69569.70
## 13 103141.00 66917.60 101416.00 76335.20 112492.00
## 14 86373.20 50789.70 81579.80 50651.90 100890.00
## 15 24155.60 21858.30 22663.70 21117.80 28080.20
## 16 33793.10 37375.50 32432.10 35091.30 33178.20
## 17 35999.00 39530.40 25782.00 27276.60 28018.50
## 18 39492.30 43574.00 24091.30 27590.80 23443.40
## 19 19732.10 25296.80 26011.40 32622.80 25877.50
## 20 35284.10 35595.50 31778.70 39017.60 32064.00
## 21 48746.10 57691.00 36047.70 44524.00 36835.40
## 22 84542.20 200646.00 39408.50 46527.70 44432.00
## 23 15019.00 31776.50 15208.70 32202.20 47421.00
## 24 32301.00 49409.40 51008.60 70826.00 65350.60
## 25 31873.40 42018.60 33441.30 43191.60 27245.70
## 26 48517.50 83228.30 51215.50 84241.30 23718.40
## 27 52029.70 54614.50 56159.50 73333.40 32350.60
## 28 29171.00 38086.00 31718.10 36835.10 32338.50
## 29 31924.30 33965.80 23803.30 31689.90 21265.40
## 30 28789.30 31285.60 28829.30 28835.50 29168.90
## 31 81590.00 91860.60 87485.30 88303.60 29178.60
## 32 43281.10 50732.00 43921.10 49294.30 59287.10
## 33 112080.00 153295.00 117377.00 158343.00 19860.30
## 34 48687.00 92031.90 47785.50 94479.60 68442.60
## 35 49642.10 57149.90 51175.10 57118.50 21139.80
## 36 38707.40 49619.10 37939.10 50710.70 46136.60
## 37 29469.50 42596.20 32683.90 42046.80 19525.40
## 38 35994.70 63960.60 30185.40 65244.80 72441.00
## 39 48432.30 71001.50 49956.10 57706.10 56283.50
## 40 73954.60 82935.60 85342.70 132464.00 89686.10
## 41 32147.60 43810.60 32373.60 45403.70 56444.90
## 42 85473.30 85720.50 84761.70 85178.40 33633.50
## 43 68086.00 79447.50 72914.80 80712.00 48508.50
## 44 67947.20 79440.50 66647.10 79188.90 42875.40
## 45 46249.70 77265.80 45416.00 71734.70 41998.40
## 46 36569.90 37239.10 37206.30 38719.20 27723.70
## 47 60732.90 73275.00 58532.10 73006.50 42454.40
## 48 31596.60 35651.30 30480.10 31575.30 21261.80
## 49 30127.70 35388.20 31161.50 37202.00 39913.10
## 50 41878.50 42222.90 42147.90 44373.70 30875.60
## 51 14865.40 16789.30 17244.80 18436.60 30263.10
## 52 33157.60 49107.10 33298.90 49695.50 49514.10
## 53 67797.20 43191.80 59713.70 43647.20 63189.80
## 54 83428.20 22600.30 23097.10 20668.30 21981.20
## 55 33053.30 30404.70 37846.10 30157.30 37521.30
## 56 28398.60 23847.80 26718.90 26559.50 35136.80
## 57 33951.90 63856.70 88119.10 66664.10 85288.50
## 58 46837.50 47217.00 72076.70 48606.80 66366.70
## 59 188445.00 57847.30 71230.20 73794.60 45177.80
## 60 59261.60 73958.10 59506.10 75544.30 50092.90
## 61 43854.50 46525.70 44429.00 50687.60 40319.90
## 62 14134.80 17978.70 15217.70 18219.70 32227.90
## 63 24394.70 36575.80 28566.10 41351.50 37826.90
## 64 31235.20 43334.10 30696.30 37882.50 30225.60
## 65 18463.50 25056.70 11092.70 39829.40 11105.40
## 66 56348.00 98100.20 49747.90 55378.70 47221.80
## 67 59015.80 80288.80 39830.50 56298.70 39104.50
## 68 80268.20 92463.00 49710.80 51710.40 47521.10
## 69 47435.90 62440.50 23891.90 27235.20 24711.40
## 70 25757.10 40005.10 22528.80 27360.30 22554.70
## 71 26630.10 38870.10 44033.70 47653.40 43388.80
## 72 19568.60 71160.30 38347.40 49887.60 38800.70
## 73 30795.10 32626.20 48646.10 59773.80 54293.70
## 74 39710.60 50141.60 26259.30 40254.20 36792.10
## 75 56850.10 38568.30 43975.60 35362.00 43741.50
## 76 65563.50 49200.30 69772.60 49381.10 71148.20
## 77 30326.40 33400.80 42936.80 20427.00 41958.60
## 78 29378.30 24016.40 34691.70 24047.70 31971.60
## 79 67352.20 36937.30 35146.90 24383.60 33606.80
## 80 90036.10 64752.50 86025.70 67484.60 87692.30
## 81 99077.80 68109.60 103275.00 69704.80 109630.00
## 82 67162.80 59464.80 66509.70 57604.50 68395.50
## 83 52900.60 50318.10 53914.80 46779.70 56175.90
## 84 36980.00 25005.00 38313.50 25811.70 40700.70
## 85 36164.10 25615.40 36701.30 25918.40 36763.30
## 86 40087.40 29806.40 39635.20 29122.90 40276.60
## 87 57296.10 53697.60 56253.90 55863.70 57913.60
## 88 55880.40 43368.90 56741.20 44344.10 58236.60
## 89 74759.80 63217.50 73648.90 63771.40 77621.50
## 90 52263.00 41265.30 53814.40 41293.20 53063.00
## 91 48452.60 19867.40 46843.30 21056.60 44963.60
## 92 34256.80 26890.50 30314.20 21879.70 27900.80
## 93 20754.70 19280.40 20981.50 18703.40 20546.20
## 94 34903.90 25594.80 35881.10 28154.70 37091.70
## 95 54735.80 39001.30 53199.50 39882.20 54323.80
## 96 34922.00 26400.80 53126.70 85468.60 51363.70
## 97 30924.80 42464.00 29810.50 43468.70 26356.70
## 98 26786.60 43466.10 103049.00 46518.20 98352.80
## 99 48682.00 69208.90 100513.00 67159.50 96896.20
## 100 71304.50 26796.50 32646.40 26827.30 30557.10
## 101 21971.70 23692.60 21490.90 23937.60 20983.10
## 102 27304.00 27506.60 26470.00 29128.70 24553.30
## 103 32729.20 42965.60 32943.40 42954.20 33363.20
## 104 38381.50 38649.80 38888.30 45542.00 39507.50
## 105 21882.70 28537.10 12015.20 28350.90 12167.00
## 106 19555.10 22673.50 19264.00 22217.90 21789.70
## 107 13905.60 37039.80 10426.90 38743.40 10608.90
## 108 21427.30 21596.60 21610.40 21768.00 21547.10
## 109 70888.00 84656.60 73787.30 83469.00 76079.90
## 110 33768.80 45245.40 36982.80 45371.00 36792.70
## 111 62761.20 77615.90 62109.00 73415.40 62595.40
## 112 37225.90 41171.50 35787.20 42398.70 35529.60
## 113 31753.60 47166.60 32532.30 47217.70 32427.10
## 114 35159.90 45330.60 34415.00 45412.00 34691.00
## 115 51192.40 72410.10 53159.90 70333.40 54865.70
## 116 20464.80 26188.60 21009.70 25822.20 21498.50
## 117 43280.60 73596.50 45208.90 86807.40 42926.90
## 118 19565.10 27608.40 19352.10 28021.10 18335.70
## 119 45005.90 50781.70 45362.30 51502.50 45901.80
## 120 30919.30 45321.40 33014.30 40038.30 31277.70
## 121 25939.30 31855.70 24467.50 31135.60 24596.10
## 122 25840.20 38250.30 25725.70 39712.80 25826.40
## 123 47614.20 70493.40 47846.50 63955.50 46614.70
## 124 60730.00 39999.20 59501.80 42398.20 59496.30
## 125 32541.20 32866.80 35036.60 34086.10 34463.50
## 126 20660.20 19528.80 20452.40 19903.70 22551.70
## 127 38566.60 35075.00 37793.90 38656.90 40466.50
## 128 68956.50 93540.20 68223.50 94058.70 88881.90
## 129 56325.10 78660.90 60990.10 81498.30 69021.30
## 130 30565.80 36205.50 30637.10 38226.30 87316.90
## 131 55267.50 75620.60 61637.50 77534.20 69468.30
## 132 27429.00 38958.30 28153.00 37198.30 35795.40
## 133 62439.30 97453.20 67023.60 96032.80 24988.60
## 134 31183.30 38775.20 33116.70 38665.00 44686.80
## 135 31823.10 35634.70 36304.40 35606.40 37132.40
## 136 32243.60 68566.70 82091.10 77740.30 81469.20
## 137 48625.50 14622.20 50789.10 14961.90 51647.80
## 138 125226.00 16481.60 19745.40 16812.80 20473.60
## 139 29120.00 40529.50 48432.20 40698.60 49030.40
## 140 34469.40 78392.40 88146.70 82557.00 87971.40
## 141 26404.60 42957.10 108332.00 45264.90 108457.00
## 142 21097.00 42707.20 65168.70 43057.90 63403.40
## 143 36500.00 36063.00 36111.60 32005.50 36141.60
## 144 63938.70 39725.50 54519.20 41078.30 54846.30
## 145 91472.40 26827.70 42395.80 22778.00 41401.30
## 146 65840.80 39492.60 44462.10 36278.70 42578.30
## 147 31537.70 36175.00 44984.30 35412.70 43175.40
## 148 23829.80 28922.10 45642.40 30629.70 44860.50
## 149 34996.10 54216.80 75530.70 57300.50 78231.30
## 150 26407.50 41848.00 43489.70 40485.80 41335.30
## 151 29649.00 29790.90 32108.50 28436.90 32213.90
## 152 41121.60 31334.50 44526.30 31960.50 45259.10
## 153 28415.00 37002.30 52387.70 37192.20 54299.70
## 154 23602.50 19361.40 23047.20 15882.00 23228.00
## 155 55744.70 98111.70 102568.00 85345.00 101909.00
## 156 68412.80 39191.10 46663.70 37757.80 50885.80
## 157 75470.70 25201.50 26936.30 25433.10 26640.70
## 158 161803.00 96544.30 126986.00 93605.40 134541.00
## 159 36820.30 66030.60 67022.70 64068.10 68089.80
## 160 49627.70 31493.70 36772.50 31399.10 36803.90
## 161 31535.40 58829.80 77942.20 58854.00 77874.20
## 162 51946.50 86990.80 115425.00 87685.40 104093.00
## 163 67952.20 48165.70 55466.40 49668.00 55862.00
## 164 73104.80 54842.60 86151.40 55151.60 97298.20
## 165 62785.20 58427.60 69147.70 59765.20 70347.40
## 166 67197.40 39746.20 52563.90 40028.20 52640.70
## 167 49784.70 23316.50 29690.40 22575.70 34477.80
## 168 21967.70 27289.40 28496.40 27548.70 32750.90
## 169 40868.30 20898.10 40579.00 30135.30 39459.70
## 170 70155.00 18473.10 23523.10 22007.00 22320.70
## 171 36153.20 19557.10 29724.30 10095.80 26996.20
## 172 49591.00 21763.70 23912.60 21924.20 24662.00
## 173 27602.10 78925.60 133563.00 75475.80 135691.00
## 174 36431.50 122884.00 156850.00 106177.00 123026.00
## 175 115308.00 30991.60 35139.20 30222.80 39391.90
## 176 35422.70 30794.30 37329.40 30791.90 40296.60
## 177 41630.80 42423.20 42832.10 44836.30 44909.50
## 178 78976.30 62565.90 97438.00 65447.20 96980.20
## 179 240996.00 31992.00 37303.40 31839.80 37069.90
## 180 32743.40 71371.50 78498.10 75548.70 79683.20
## 181 61429.70 53477.30 56585.60 53574.50 56598.80
## 182 55623.30 47079.40 54214.10 48046.00 52683.00
## 183 80834.70 42102.90 80505.70 35338.10 81886.80
## 184 52415.20 26414.70 39673.10 26455.80 37468.80
## 185 24644.20 18790.20 33520.60 17723.20 30893.00
## 186 33803.90 24412.10 31798.80 22233.20 32036.70
## 187 63386.00 41401.90 42105.20 40207.20 40895.10
## 188 68329.80 56998.00 62990.40 61947.30 67181.20
## 189 62627.60 31388.90 37559.50 26236.00 34060.50
## 190 79201.50 60043.40 85656.30 59518.80 85346.90
## 191 108372.00 52935.40 84312.70 54219.80 83414.40
## 192 48689.70 46430.30 56154.40 46531.80 55049.30
## 193 93227.90 71460.60 86599.80 67648.80 74588.30
## 194 52244.40 59055.00 59584.50 56869.60 57822.20
## 195 45463.70 51520.40 71812.30 53331.20 74868.30
## 196 33120.40 28741.30 40437.40 30894.70 41083.80
## 197 56290.50 28439.30 30957.00 24520.70 28670.00
## 198 70181.70 30114.50 40862.80 33036.20 40150.20
## 199 45031.70 19448.90 23531.50 21644.50 23705.00
## 200 71435.20 31410.50 44552.70 30901.20 43959.60
## 201 23380.90 64990.90 24874.40 49089.50 28454.30
## 202 34404.40 59071.10 46513.90 55881.80 103488.00
## 203 50587.70 72825.90 53688.10 75435.70 62703.80
## 204 16322.10 28672.10 13392.30 26326.00 46230.60
## 205 34937.20 39481.60 35294.40 40109.80 66471.30
## 206 39486.10 43807.30 41330.30 41528.60 29153.50
## 207 21389.60 21878.40 22031.60 22690.50 18685.60
## 208 63339.90 72954.70 47373.40 73596.70 28652.40
## 209 19315.70 34691.00 24182.10 30456.70 35646.20
## 210 56493.40 73701.60 34210.80 38050.70 35481.30
## 211 42655.20 44846.90 73418.10 86405.50 70718.10
## 212 38312.20 41181.60 60677.80 74263.20 61860.70
## 213 39999.30 45325.00 33971.70 50804.30 35268.20
## 214 25825.50 26650.40 33908.00 33533.00 35975.00
## 215 27909.20 35882.50 26791.60 36335.90 44506.10
## 216 39532.40 42230.80 40611.80 41688.20 44978.80
## 217 28053.10 48039.20 28614.70 37740.50 27354.60
## 218 45091.60 70966.00 40793.10 67876.00 36347.70
## 219 24677.10 30909.20 25195.80 30817.40 20973.70
## 220 49255.70 59380.90 53811.40 70225.90 42419.80
## 221 24680.80 28481.20 23879.80 25263.60 26495.00
## 222 36906.30 42855.90 32586.00 44779.30 27694.10
## 223 27099.90 43560.80 23105.10 42434.70 61442.20
## 224 32824.10 56308.40 52655.60 53641.00 31240.20
## 225 66335.10 77271.40 67948.40 71697.00 52370.60
## 226 23506.70 28607.40 24000.20 27963.20 53991.40
## 227 30450.00 46422.40 37793.90 44270.00 53860.50
## 228 36251.20 48615.50 37058.50 44032.10 28033.70
## 229 64813.20 77313.90 61534.40 77985.00 18880.60
## 230 81814.50 126741.00 82072.90 121776.00 26353.70
## 231 21669.90 25331.60 22742.30 26534.10 47119.10
## 232 44322.00 59487.30 47105.20 57384.80 29327.90
## 233 24043.70 36515.10 25469.70 35989.90 44678.60
## 234 20504.30 24751.20 20763.40 27287.70 32879.60
## 235 45873.90 67087.00 45781.60 72430.40ddata1 <- decompose(tsdata1, "multiplicative")
plot(ddata1)
plot(ddata1$seasonal)
plot(ddata1$trend)
plot(newsal$avg_wage_ft,type ="l",main = "Average salary of both genders",xlab= "Year",ylab="Average Salary")
class(tsdata1)## [1] "ts"Arima model
mymodel1 <- auto.arima(tsdata1)
mymodel1## Series: tsdata1
## ARIMA(4,0,4)(2,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 ar3 ar4 ma1 ma2 ma3 ma4
## 0.5324 -0.1580 -0.0477 0.0974 -0.0894 0.7613 0.0705 0.5717
## s.e. 0.0346 0.0395 0.0478 0.0382 0.0294 0.0295 0.0289 0.0292
## sar1 sar2 mean
## 0.0939 0.0394 48299.046
## s.e. 0.0220 0.0197 1352.806
##
## sigma^2 estimated as 241847956: log likelihood=-31204.48
## AIC=62432.96 AICc=62433.08 BIC=62504.29fitting model
auto.arima(tsdata1,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 62683.07
## ARIMA(0,0,0) with non-zero mean : 65316.82
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 63711.67
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 64602.49
## ARIMA(0,0,0) with zero mean : 69530.31
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 62729.14
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 62687.61
## ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 62641.8
## ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 62641.47
## ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 62938.67
## ARIMA(2,0,1)(2,0,0)[12] with non-zero mean : 62646.66
## ARIMA(3,0,2)(2,0,0)[12] with non-zero mean : 62620.82
## ARIMA(3,0,2)(1,0,0)[12] with non-zero mean : 62655.7
## ARIMA(3,0,2)(2,0,1)[12] with non-zero mean : 62620.9
## ARIMA(3,0,2)(1,0,1)[12] with non-zero mean : 62651.38
## ARIMA(3,0,1)(2,0,0)[12] with non-zero mean : 62638.49
## ARIMA(4,0,2)(2,0,0)[12] with non-zero mean : 62615.38
## ARIMA(4,0,2)(1,0,0)[12] with non-zero mean : 62678.02
## ARIMA(4,0,2)(2,0,1)[12] with non-zero mean : Inf
## ARIMA(4,0,2)(1,0,1)[12] with non-zero mean : 62597.9
## ARIMA(4,0,2)(0,0,1)[12] with non-zero mean : 62704.74
## ARIMA(4,0,2)(1,0,2)[12] with non-zero mean : 62595.29
## ARIMA(4,0,2)(0,0,2)[12] with non-zero mean : 62705.22
## ARIMA(4,0,2)(2,0,2)[12] with non-zero mean : Inf
## ARIMA(3,0,2)(1,0,2)[12] with non-zero mean : 62652.8
## ARIMA(4,0,1)(1,0,2)[12] with non-zero mean : 62617.43
## ARIMA(5,0,2)(1,0,2)[12] with non-zero mean : 62473.82
## ARIMA(5,0,2)(0,0,2)[12] with non-zero mean : 62522.88
## ARIMA(5,0,2)(1,0,1)[12] with non-zero mean : 62471.83
## ARIMA(5,0,2)(0,0,1)[12] with non-zero mean : 62521.76
## ARIMA(5,0,2)(1,0,0)[12] with non-zero mean : 62472.17
## ARIMA(5,0,2)(2,0,1)[12] with non-zero mean : 62450.5
## ARIMA(5,0,2)(2,0,0)[12] with non-zero mean : 62450.12
## ARIMA(5,0,1)(2,0,0)[12] with non-zero mean : 62589.76
## ARIMA(5,0,3)(2,0,0)[12] with non-zero mean : 62416.7
## ARIMA(5,0,3)(1,0,0)[12] with non-zero mean : 62437.74
## ARIMA(5,0,3)(2,0,1)[12] with non-zero mean : 62416.72
## ARIMA(5,0,3)(1,0,1)[12] with non-zero mean : 62437.48
## ARIMA(4,0,3)(2,0,0)[12] with non-zero mean : 62519.53
## ARIMA(5,0,4)(2,0,0)[12] with non-zero mean : 62369.54
## ARIMA(5,0,4)(1,0,0)[12] with non-zero mean : 62422.97
## ARIMA(5,0,4)(2,0,1)[12] with non-zero mean : 62371.23
## ARIMA(5,0,4)(1,0,1)[12] with non-zero mean : 62421.6
## ARIMA(4,0,4)(2,0,0)[12] with non-zero mean : 62368.15
## ARIMA(4,0,4)(1,0,0)[12] with non-zero mean : 62456.66
## ARIMA(4,0,4)(2,0,1)[12] with non-zero mean : 62369.51
## ARIMA(4,0,4)(1,0,1)[12] with non-zero mean : 62456.13
## ARIMA(3,0,4)(2,0,0)[12] with non-zero mean : 62374.23
## ARIMA(4,0,5)(2,0,0)[12] with non-zero mean : Inf
## ARIMA(3,0,3)(2,0,0)[12] with non-zero mean : 62554.78
## ARIMA(3,0,5)(2,0,0)[12] with non-zero mean : Inf
## ARIMA(5,0,5)(2,0,0)[12] with non-zero mean : Inf
## ARIMA(4,0,4)(2,0,0)[12] with zero mean : 62664.08
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(4,0,4)(2,0,0)[12] with non-zero mean : 62432.96
##
## Best model: ARIMA(4,0,4)(2,0,0)[12] with non-zero mean## Series: tsdata1
## ARIMA(4,0,4)(2,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ar2 ar3 ar4 ma1 ma2 ma3 ma4
## 0.5324 -0.1580 -0.0477 0.0974 -0.0894 0.7613 0.0705 0.5717
## s.e. 0.0346 0.0395 0.0478 0.0382 0.0294 0.0295 0.0289 0.0292
## sar1 sar2 mean
## 0.0939 0.0394 48299.046
## s.e. 0.0220 0.0197 1352.806
##
## sigma^2 estimated as 241847956: log likelihood=-31204.48
## AIC=62432.96 AICc=62433.08 BIC=62504.29Residuals
library(tseries)
adf.test(mymodel1$residuals)## Warning in adf.test(mymodel1$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel1$residuals
## Dickey-Fuller = -13.335, Lag order = 14, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel1$residuals)
acf(ts(mymodel1$residuals),main = "ACF Residual")
pacf(ts(mymodel1$residuals),main = "PACF Residual")
#time series forecasting and average salary for both genders
myforecast1 <- forecast(mymodel1, level =c (95), h= 3*12)
plot(myforecast1,main = "Salary forecasting for the next 3years",xlab = "Time", ylab ="Average salary")
myforecast1[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 235
## 236 55624.57 46884.93 53731.15 49696.10 48435.46 46998.95 49289.00
## 237 48459.07 47592.97 48275.17 47868.02 47755.90 48104.21 49086.30
## 238 48602.55 48177.07 48510.69 48313.59 48253.43 48229.57 48411.94
## Aug Sep Oct Nov Dec
## 235 46477.44
## 236 47347.17 49429.38 47061.29 49742.96 47532.72
## 237 48114.71 49144.91 48083.74 49384.94 48155.50
## 238 48244.26 48422.98 48230.09 48457.86Male average salary
library(forecast)
library(readr)
library(zoo)
newsal <- drop_na(Sal)
msal <- subset(newsal, sex_name == "Male", select = c(avg_wage_ft, year,soc_name,sex_name))
head(msal)msal$avg_wage_ft <- as.numeric(msal$avg_wage_ft)
msal$Date <- paste (msal$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata2 <- ts(msal$avg_wage_ft,frequency = 12)
tsdata2## Jan Feb Mar Apr May Jun Jul
## 1 86682.40 85707.40 85267.30 51929.70 49598.00 50787.10 178424.00
## 2 66271.40 70643.50 74757.40 103035.00 101524.00 95358.60 63128.90
## 3 70865.60 57713.90 57285.50 39245.60 42177.00 45607.10 65784.40
## 4 81777.00 84362.90 84184.80 50066.80 49178.10 50804.80 114071.00
## 5 35993.20 30974.90 56441.90 31216.40 33891.00 37167.80 43099.00
## 6 62677.00 64969.30 66329.80 80998.70 87303.10 88056.70 115709.00
## 7 58937.00 60610.60 55121.80 103141.00 101416.00 112492.00 68698.10
## 8 64332.90 79193.40 104016.00 22098.40 21858.30 21117.80 37987.60
## 9 35091.30 35086.40 35455.50 44099.10 40037.90 39530.40 25782.00
## 10 27590.80 28776.30 27830.30 26104.10 25778.40 25296.80 32622.80
## 11 39017.60 47352.10 46781.80 57650.40 55980.80 57691.00 36047.70
## 12 39408.50 46527.70 44432.00 41292.60 42417.60 43499.10 31244.30
## 13 32468.70 32301.00 70826.00 73451.00 74795.70 67236.50 40348.10
## 14 72404.30 83228.30 84241.30 31178.10 30338.30 31498.20 77211.20
## 15 33303.60 29171.00 31718.10 32338.50 31612.00 32333.10 29171.10
## 16 30289.30 28789.30 28835.50 34437.90 34740.20 34290.90 103558.00
## 17 49411.20 50732.00 49294.30 59287.10 58387.50 62070.00 147386.00
## 18 95376.10 92031.90 94479.60 90281.20 89507.80 93124.60 57772.80
## 19 50389.30 49619.10 50710.70 53101.60 50188.00 52099.30 30748.90
## 20 80767.10 63960.60 65244.80 109032.00 104883.00 105495.00 62873.40
## 21 90825.90 82935.60 85342.70 98374.30 100899.00 103552.00 42945.60
## 22 83858.90 85473.30 85178.40 42615.00 42966.10 42349.40 78912.60
## 23 80560.80 79440.50 79188.90 43224.20 43356.20 43244.00 70464.30
## 24 36532.00 37239.10 38719.20 28939.00 29296.40 29635.60 74143.00
## 25 39053.40 31596.60 30480.10 22975.20 22900.50 23543.40 34313.30
## 26 42698.80 41878.50 44373.70 38262.10 35025.50 39610.50 13036.90
## 27 51499.00 49107.10 49695.50 49514.10 48498.60 48604.00 66689.30
## 28 74121.90 77619.80 83428.20 23097.10 20668.30 20057.10 30990.90
## 29 25750.50 26901.10 28398.60 23847.80 35136.80 64308.40 32571.00
## 30 41768.90 41615.20 46837.50 72076.70 66366.70 74604.80 166196.00
## 31 57316.40 57447.30 56718.50 73800.00 73958.10 75544.30 55437.20
## 32 40319.90 41757.60 47655.60 17772.50 17978.70 18219.70 37704.50
## 33 47082.30 47294.80 48116.60 40090.40 42263.30 43334.10 37882.50
## 34 39829.40 42113.70 48268.80 78031.20 98212.30 98100.20 55378.70
## 35 39830.50 39104.50 42959.10 90671.60 89170.80 92463.00 49710.80
## 36 27235.20 26530.60 26230.40 40313.60 39379.80 40005.10 27360.30
## 37 47653.40 46796.30 46241.10 71574.80 73100.40 71160.30 49887.60
## 38 59773.80 59945.50 54194.90 50799.70 49596.70 50141.60 40254.20
## 39 43975.60 43741.50 41947.70 72987.60 72236.70 65563.50 69772.60
## 40 42936.80 41958.60 42585.70 35529.80 31803.80 29378.30 34691.70
## 41 36937.30 35146.90 33606.80 34355.90 32555.60 31251.20 90036.10
## 42 99077.80 103275.00 109630.00 69936.90 69457.40 67449.90 66083.00
## 43 52900.60 53914.80 56175.90 53013.20 53128.60 51918.80 36980.00
## 44 36164.10 36701.30 36763.30 63776.20 63217.30 65117.60 40087.40
## 45 57296.10 56253.90 57913.60 79554.70 81774.20 83199.10 55880.40
## 46 74759.80 73648.90 77621.50 59112.60 59724.30 58455.30 52263.00
## 47 48452.60 46843.30 44963.60 54521.70 55223.60 56766.00 34256.80
## 48 20754.70 19280.40 20546.20 19040.30 18842.30 18939.60 34903.90
## 49 54735.80 53199.50 54323.80 31276.20 31908.50 31894.00 34830.10
## 50 39988.10 33655.50 30924.80 29810.50 26356.70 23179.20 25031.60
## 51 45448.30 48682.00 100513.00 96896.20 96809.40 46683.60 53889.30
## 52 21893.80 24262.40 23692.60 23937.60 25323.00 38987.10 32569.10
## 53 32220.10 45623.00 42965.60 42954.20 44294.50 36086.30 36551.70
## 54 43090.60 44611.20 28537.10 28350.90 24146.50 25052.40 21211.70
## 55 31250.20 31553.50 37039.80 38743.40 36511.00 22856.10 20585.60
## 56 49016.40 49691.60 84656.60 83469.00 81530.90 64026.20 64505.00
## 57 73078.80 74917.30 77615.90 73415.40 70774.20 30047.20 30276.70
## 58 91059.80 96155.50 47166.60 47217.70 47446.70 30678.20 32521.10
## 59 75587.30 76563.50 72410.10 70333.40 70280.40 29988.20 32453.30
## 60 37665.80 41866.80 73596.50 86807.40 84098.70 28134.50 28860.60
## 61 155950.00 157104.00 50781.70 51502.50 50641.50 30015.50 26714.90
## 62 45522.20 44898.30 31855.70 31135.60 28482.00 59762.90 57690.60
## 63 61959.60 76301.40 70493.40 63955.50 65700.20 39542.90 37354.70
## 64 28854.50 29221.80 32541.20 32866.80 34463.50 40927.20 40884.50
## 65 33030.30 33880.70 38566.60 37793.90 38656.90 38993.60 36626.60
## 66 89259.90 92833.80 77179.00 78660.90 81498.30 103208.00 104354.00
## 67 97187.20 93234.30 74851.40 75620.60 77534.20 104462.00 101133.00
## 68 43039.20 41204.90 101621.00 97453.20 96032.80 24988.60 19433.60
## 69 51712.60 59460.10 31558.20 27825.00 31823.10 35634.70 37132.40
## 70 81469.20 83952.80 47326.70 47874.90 48625.50 50789.10 51647.80
## 71 20473.60 21062.50 31014.20 29276.40 29120.00 48432.20 49030.40
## 72 87971.40 92511.00 26292.30 26624.00 26404.60 42957.10 45264.90
## 73 63403.40 61425.30 36704.20 36022.70 36500.00 36111.60 32005.50
## 74 54846.30 59168.10 91635.20 91495.50 91472.40 42395.80 41401.30
## 75 42578.30 43462.90 33617.30 31877.40 31537.70 44984.30 43175.40
## 76 44860.50 46920.40 34435.20 34004.50 34996.10 75530.70 78231.30
## 77 41335.30 40242.60 28472.20 29621.10 29649.00 29790.90 28436.90
## 78 45259.10 54121.50 30421.30 28029.20 28415.00 52387.70 54299.70
## 79 23228.00 26360.50 44992.70 49459.80 55744.70 98111.70 85345.00
## 80 50885.80 56186.40 68698.50 68612.10 74978.00 26936.30 26640.70
## 81 134541.00 122741.00 36492.90 36172.70 36820.30 67022.70 68089.80
## 82 36803.90 35915.50 31951.00 30930.70 31459.20 77942.20 77874.20
## 83 104093.00 123099.00 65982.40 66450.10 67952.20 55466.40 55862.00
## 84 97298.20 90578.60 59398.40 61327.30 62785.20 69147.70 70347.40
## 85 52640.70 52277.40 58631.90 53169.20 49784.70 29690.40 34477.80
## 86 27548.70 28446.00 36489.20 38251.90 40868.30 40579.00 39459.70
## 87 22320.70 21408.60 40163.70 39559.10 36153.20 29724.30 26996.20
## 88 21924.20 23476.50 30084.70 28021.10 27602.10 133563.00 135691.00
## 89 106177.00 124726.00 118203.00 116035.00 115308.00 35139.20 39391.90
## 90 40296.60 45581.90 32427.90 36552.40 41630.80 42832.10 44836.30
## 91 96980.20 107782.00 229096.00 233337.00 240996.00 37303.40 37069.90
## 92 79683.20 87999.50 60370.80 60370.00 61429.70 56585.60 56598.80
## 93 52683.00 52961.20 78722.70 77208.00 80834.70 80505.70 81886.80
## 94 37468.80 37028.60 19187.00 20829.70 24644.20 33520.60 30893.00
## 95 32036.70 33117.80 63222.60 62588.50 63386.00 42105.20 40207.20
## 96 67181.20 67480.70 56995.30 57334.80 62627.60 37559.50 34060.50
## 97 85346.90 95234.00 95693.80 98623.80 108372.00 84312.70 83414.40
## 98 55049.30 55830.10 91862.10 96020.30 93227.90 86599.80 74588.30
## 99 57822.20 65632.70 34663.90 39032.10 45463.70 71812.30 74868.30
## 100 41083.80 38117.20 119092.00 55356.20 56290.50 28439.30 28670.00
## 101 40150.20 39751.00 56332.10 56558.80 45031.70 23531.50 23705.00
## 102 43959.60 44295.60 45572.90 46700.70 36016.70 59686.80 64990.90
## 103 59071.10 55881.80 103488.00 91961.20 99695.50 70888.10 72825.90
## 104 28672.10 26326.00 48520.70 48194.00 48187.70 40494.50 39481.60
## 105 39486.10 41528.60 30964.80 30144.50 30058.60 20040.90 21878.40
## 106 72954.70 73596.70 31404.90 30397.90 30310.70 32737.80 34691.00
## 107 78920.50 73701.60 38050.70 40196.50 40323.10 47064.40 46718.30
## 108 37094.80 38312.20 74263.20 67482.10 62392.30 44549.40 43587.90
## 109 26849.70 26650.40 33908.00 33533.00 35975.00 31813.00 35351.20
## 110 53066.00 51574.20 40437.10 39532.40 41688.20 51848.60 53466.00
## 111 32327.80 29358.80 73259.60 70966.00 67876.00 59420.40 61474.40
## 112 31043.60 31319.90 62947.20 59380.90 70225.90 42419.80 42027.80
## 113 36322.80 37546.60 41355.00 42855.90 44779.30 28740.70 27196.70
## 114 61512.90 62290.20 2002.53 2027.83 49199.10 41721.80 105603.00
## 115 48650.90 83414.50 77271.40 71697.00 57278.90 56145.60 59369.60
## 116 66103.40 42773.80 46422.40 44270.00 53860.50 51525.70 51822.60
## 117 29424.50 77743.00 77313.90 77985.00 18880.60 20527.40 24554.60
## 118 27583.10 25065.70 25331.60 26534.10 47119.10 46707.10 47588.50
## 119 36839.20 37276.80 36515.10 35989.90 51464.50 52686.40 50023.60
## 120 34160.10 65888.30 67087.00 72430.40
## Aug Sep Oct Nov Dec
## 1 175534.00 177805.00 48778.20 31693.80 35615.50
## 2 59704.90 63631.80 92254.20 91961.30 90847.90
## 3 70146.20 72558.50 46509.80 46152.10 49015.60
## 4 117501.00 121559.00 39549.50 38154.10 45188.80
## 5 35946.40 43311.30 84891.40 74257.20 78098.90
## 6 120292.00 122867.00 57660.30 60639.70 69569.70
## 7 68460.00 73682.90 86373.20 81579.80 100890.00
## 8 39227.20 40428.60 31610.70 33793.10 37375.50
## 9 28018.50 29367.60 43058.00 42790.00 43574.00
## 10 32814.80 34589.20 34664.70 34310.50 35595.50
## 11 36835.40 45596.10 82364.60 81726.20 84542.20
## 12 31776.50 32202.20 47421.00 48296.30 48689.80
## 13 42018.60 43191.60 31949.10 30396.10 30816.10
## 14 52029.70 56159.50 32350.60 31872.40 33266.50
## 15 31924.30 31689.90 27833.70 28314.80 28807.70
## 16 91860.60 88303.60 46751.60 47027.00 46231.00
## 17 153295.00 158343.00 22086.00 21086.80 21237.60
## 18 57149.90 57118.50 25126.90 24283.20 25006.70
## 19 29469.50 32683.90 20940.80 22047.40 22863.50
## 20 71001.50 57706.10 72414.50 70993.60 67459.30
## 21 43810.60 45403.70 78799.30 76532.70 81124.70
## 22 79447.50 80712.00 53489.30 53279.70 53781.80
## 23 77265.80 71734.70 54163.90 52371.40 53941.70
## 24 73275.00 73006.50 42454.40 45848.90 46418.60
## 25 35388.20 37202.00 67338.40 66080.70 63924.40
## 26 14865.40 18436.60 42302.20 41039.40 39008.10
## 27 67387.50 67797.20 59713.70 63189.80 67369.30
## 28 31718.40 33053.30 37846.10 37521.30 37069.30
## 29 34649.50 33951.90 88119.10 85288.50 83640.60
## 30 181351.00 188445.00 57847.30 71230.20 73794.60
## 31 53795.50 58516.00 47055.80 46525.70 50687.60
## 32 37518.10 39116.90 33005.80 36575.80 41351.50
## 33 38292.40 38937.50 24943.20 25056.90 25056.70
## 34 49621.80 48605.60 81918.30 79195.10 80288.80
## 35 47521.10 48570.60 58145.50 58681.00 62440.50
## 36 26470.20 24371.50 38832.00 38817.70 38870.10
## 37 50174.70 52365.10 27644.00 28776.20 30795.10
## 38 36792.10 39203.80 57192.40 57424.50 56850.10
## 39 71148.20 68058.00 35305.30 34485.90 30326.40
## 40 31971.60 29666.00 60794.50 60848.80 67352.20
## 41 86025.70 87692.30 169056.00 162353.00 186121.00
## 42 66509.70 68395.50 67142.20 65911.90 66242.30
## 43 38313.50 40700.70 90773.00 87517.40 88172.00
## 44 39635.20 40276.60 37238.70 39369.60 40531.60
## 45 56741.20 58236.60 54065.90 50821.20 51803.10
## 46 53814.40 53063.00 56839.40 55665.90 55471.40
## 47 26890.50 21879.70 27846.00 26253.50 27351.20
## 48 35881.10 37091.70 25829.10 26847.90 28193.80
## 49 34922.00 26400.80 85468.60 71721.70 69822.40
## 50 25347.90 103049.00 98352.80 99102.10 47039.10
## 51 61653.30 32646.40 30557.10 30640.70 23184.30
## 52 33331.30 27506.60 29128.70 33769.50 32220.40
## 53 35938.10 38649.80 38888.30 39507.50 42318.10
## 54 21782.20 22673.50 22217.90 21789.70 31694.70
## 55 20831.50 21596.60 21610.40 22104.10 45515.70
## 56 67854.70 45245.40 45371.00 45688.70 74577.40
## 57 31325.20 41171.50 42398.70 43037.70 79669.50
## 58 29577.70 45330.60 45412.00 44340.80 78185.10
## 59 37544.50 26188.60 25822.20 26765.50 37854.20
## 60 29274.40 27608.40 28021.10 31221.20 153323.00
## 61 28766.20 45321.40 40038.30 38624.00 45885.30
## 62 51806.20 38250.30 39712.80 40619.60 64225.50
## 63 38919.20 60730.00 59501.80 59496.30 38234.00
## 64 43870.00 20660.20 20452.40 22551.70 32545.30
## 65 38812.30 93881.10 93540.20 94058.70 91288.90
## 66 103835.00 36401.80 36205.50 38226.30 87316.90
## 67 99247.20 41110.60 38958.30 37198.30 45423.20
## 68 41121.20 40054.00 38775.20 38665.00 52041.60
## 69 39567.50 28745.10 30590.40 32243.60 82091.10
## 70 56131.20 35222.80 37073.30 41991.60 19745.40
## 71 55409.90 30657.20 32449.40 34469.40 88146.70
## 72 49830.20 20867.50 20181.90 21097.00 65168.70
## 73 40704.40 63497.80 64617.30 63938.70 54519.20
## 74 36636.20 80534.10 74850.20 65840.80 44462.10
## 75 44185.30 22929.10 22955.90 23829.80 45642.40
## 76 71518.10 19060.30 24793.60 26407.50 41848.00
## 77 28301.80 39003.80 39860.30 41121.60 44526.30
## 78 54219.60 25140.20 26301.90 23602.50 23047.20
## 79 91465.80 66088.00 66200.80 68412.80 46663.70
## 80 26502.80 186196.00 176829.00 161803.00 126986.00
## 81 72910.40 34899.90 30870.30 49627.70 36772.50
## 82 77105.30 51009.40 49866.90 51946.50 115425.00
## 83 51949.50 72590.90 70595.50 73104.80 86151.40
## 84 79300.90 65670.70 66565.20 67197.40 52563.90
## 85 35650.80 21187.40 21998.00 21967.70 28496.40
## 86 42461.20 73812.80 69092.80 70155.00 23523.10
## 87 28491.20 46889.90 48855.70 49591.00 23912.60
## 88 139495.00 36413.90 35857.50 36431.50 156850.00
## 89 31503.80 39295.70 33565.40 35422.70 37329.40
## 90 47819.70 83125.70 78467.90 78976.30 97438.00
## 91 37348.30 27210.20 27138.10 32743.40 78498.10
## 92 54972.40 57133.60 55722.10 55623.30 54214.10
## 93 85486.20 50765.00 52479.30 52415.20 39673.10
## 94 27293.90 33334.90 32688.30 33803.90 31798.80
## 95 39376.30 61966.20 65856.30 68329.80 62990.40
## 96 35804.70 78612.70 79930.90 79201.50 85656.30
## 97 95578.30 70485.30 50791.40 48689.70 56154.40
## 98 78735.20 50813.40 49845.40 50231.10 59584.50
## 99 76526.20 31560.90 32816.10 33120.40 40437.40
## 100 30686.50 70718.10 68353.40 70181.70 40862.80
## 101 25651.80 71470.50 71054.80 71435.20 44552.70
## 102 49089.50 28454.30 29237.90 29358.10 52557.60
## 103 75435.70 77015.90 77255.00 79527.00 26417.60
## 104 40109.80 92986.90 92891.80 102640.00 40517.60
## 105 22690.50 35372.90 36388.80 37546.60 76635.90
## 106 30456.70 35646.20 33336.90 35445.30 81716.10
## 107 44846.90 86405.50 84648.40 86606.00 40384.20
## 108 45325.00 50804.30 47785.90 47750.30 27514.90
## 109 34929.50 37961.80 27909.20 26791.60 51411.30
## 110 54422.70 39850.60 48039.20 37740.50 29322.70
## 111 59139.90 30658.40 30909.20 30817.40 31692.40
## 112 44274.20 26811.10 28481.20 25263.60 34543.80
## 113 27462.10 39789.20 43560.80 42434.70 61442.20
## 114 56308.40 52655.60 53641.00 38502.30 50883.30
## 115 31673.50 28607.40 27963.20 53991.40 56756.50
## 116 43913.30 48615.50 44032.10 31509.90 28937.80
## 117 131111.00 126741.00 121776.00 26353.70 23047.80
## 118 64753.80 59487.30 57384.80 35983.70 36495.40
## 119 24908.80 24751.20 27287.70 34129.40 34563.40
## 120ddata2 <- decompose(tsdata2)
plot(ddata2)
plot(ddata2$seasonal)
plot(ddata2$trend)
#actual plot
plot(msal$avg_wage_ft,type ="l",main = " Male Average salary",xlab= "Year",ylab="Average Salary")
#class
class(tsdata2)## [1] "ts"arima model
mymodel2 <- auto.arima(tsdata2)
mymodel2## Series: tsdata2
## ARIMA(1,0,3)(2,0,2)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 ma2 ma3 sar1 sar2 sma1 sma2
## 0.8987 -0.0612 -0.0062 -0.5274 -1.1454 -0.4460 1.1298 0.4303
## s.e. 0.0387 0.0486 0.0429 0.0382 0.3098 0.3749 0.3222 0.3837
## mean
## 53491.76
## s.e. 1885.79
##
## sigma^2 estimated as 3.3e+08: log likelihood=-16072.17
## AIC=32164.34 AICc=32164.5 BIC=32217.01fitting the best model
auto.arima(tsdata2,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 32129.98
## ARIMA(0,0,0) with non-zero mean : 33374.1
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 32356.22
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 32761.37
## ARIMA(0,0,0) with zero mean : 35583.8
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 32174.49
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 32129.05
## ARIMA(2,0,2) with non-zero mean : 32172.54
## ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 32132.99
## ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 32134.92
## ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 32129.81
## ARIMA(2,0,1)(1,0,0)[12] with non-zero mean : 32335.56
## ARIMA(3,0,2)(1,0,0)[12] with non-zero mean : 32130.73
## ARIMA(2,0,3)(1,0,0)[12] with non-zero mean : 32114.83
## ARIMA(2,0,3) with non-zero mean : 32155.33
## ARIMA(2,0,3)(2,0,0)[12] with non-zero mean : 32121.28
## ARIMA(2,0,3)(1,0,1)[12] with non-zero mean : 32114.6
## ARIMA(2,0,3)(0,0,1)[12] with non-zero mean : 32157.22
## ARIMA(2,0,3)(2,0,1)[12] with non-zero mean : 32123.09
## ARIMA(2,0,3)(1,0,2)[12] with non-zero mean : 32115.58
## ARIMA(2,0,3)(0,0,2)[12] with non-zero mean : 32157.79
## ARIMA(2,0,3)(2,0,2)[12] with non-zero mean : 32112.21
## ARIMA(1,0,3)(2,0,2)[12] with non-zero mean : 32108.97
## ARIMA(1,0,3)(1,0,2)[12] with non-zero mean : 32112.99
## ARIMA(1,0,3)(2,0,1)[12] with non-zero mean : 32120.39
## ARIMA(1,0,3)(1,0,1)[12] with non-zero mean : 32112.03
## ARIMA(0,0,3)(2,0,2)[12] with non-zero mean : 32135.89
## ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : 32137.78
## ARIMA(1,0,4)(2,0,2)[12] with non-zero mean : 32110.89
## ARIMA(0,0,2)(2,0,2)[12] with non-zero mean : 32141.84
## ARIMA(0,0,4)(2,0,2)[12] with non-zero mean : 32134.75
## ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 32136.89
## ARIMA(2,0,4)(2,0,2)[12] with non-zero mean : 32121.52
## ARIMA(1,0,3)(2,0,2)[12] with zero mean : Inf
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,0,3)(2,0,2)[12] with non-zero mean : 32164.34
##
## Best model: ARIMA(1,0,3)(2,0,2)[12] with non-zero mean## Series: tsdata2
## ARIMA(1,0,3)(2,0,2)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 ma2 ma3 sar1 sar2 sma1 sma2
## 0.8987 -0.0612 -0.0062 -0.5274 -1.1454 -0.4460 1.1298 0.4303
## s.e. 0.0387 0.0486 0.0429 0.0382 0.3098 0.3749 0.3222 0.3837
## mean
## 53491.76
## s.e. 1885.79
##
## sigma^2 estimated as 3.3e+08: log likelihood=-16072.17
## AIC=32164.34 AICc=32164.5 BIC=32217.01Residuals
library(tseries)
adf.test(mymodel2$residuals)## Warning in adf.test(mymodel2$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel2$residuals
## Dickey-Fuller = -10.304, Lag order = 11, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel2$residuals)
acf(ts(mymodel2$residuals),main = "ACF Residual")
pacf(ts(mymodel2$residuals),main = "PACF Residual")
#forecasting male salary and mean
myforecast2 <- forecast(mymodel2, level =c (95), h= 3*12)
plot(myforecast2,main = " Male Salary forecasting for the next 3years",xlab = "Time", ylab ="Average salary")
myforecast2[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 120 56204.39 55175.57 52489.47
## 121 53455.69 52496.59 52492.91 52450.65 53340.98 53364.44 53472.37
## 122 53269.69 53710.18 53697.72 53709.55 53298.04 53323.22 53322.71
## 123 53425.87 53383.15 53429.67 53462.48
## Aug Sep Oct Nov Dec
## 120 53530.29 53556.97 53587.43 53300.74 53333.17
## 121 52856.12 52902.87 52919.99 53277.89 53313.30
## 122 53629.00 53621.66 53640.67 53405.52 53392.66
## 123Female average salary
library(forecast)
library(readr)
library(zoo)
newsal <- drop_na(Sal)
fsal <- subset(newsal, sex_name == "Female", select = c(avg_wage_ft, year,soc_name,sex_name))
head(fsal)fsal$avg_wage_ft <- as.numeric(fsal$avg_wage_ft)
fsal$Date <- paste (fsal$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata3 <- ts(fsal$avg_wage_ft,frequency = 12)
tsdata3## Jan Feb Mar Apr May Jun Jul
## 1 55458.50 54431.80 55109.50 18393.40 18262.40 17693.80 99920.40
## 2 58112.50 56859.20 56820.50 60364.50 56464.60 58797.30 53526.10
## 3 43737.20 44096.90 46257.90 33057.80 40732.10 37884.60 42522.10
## 4 79105.70 73835.60 75047.70 40489.90 28436.40 25723.20 53670.20
## 5 21655.00 20995.30 24421.60 33578.50 31982.60 30460.30 27280.20
## 6 48093.40 47688.70 49037.10 52731.10 50930.70 54658.70 108388.00
## 7 39494.80 36048.70 32984.60 66644.30 66917.60 76335.20 40377.10
## 8 73956.70 75341.10 74997.10 24155.60 22663.70 28080.20 27475.10
## 9 34173.20 36675.70 35999.00 27276.60 28303.80 28284.00 40103.30
## 10 18904.00 20467.70 19732.10 26011.40 25877.50 28655.30 33819.20
## 11 53531.90 52171.90 48746.10 44524.00 48361.30 42067.60 117369.00
## 12 15019.00 15208.70 48312.50 45628.70 45393.80 52017.90 49409.40
## 13 31873.40 33441.30 27245.70 26452.10 26328.70 47082.80 48517.50
## 14 54614.50 73333.40 51424.40 20025.30 19912.70 35392.40 38086.00
## 15 33965.80 23803.30 21265.40 21384.80 21870.20 31249.60 31285.60
## 16 81590.00 87485.30 29178.60 23489.80 27868.60 47186.70 43281.10
## 17 112080.00 117377.00 19860.30 19992.10 20871.90 50021.20 48687.00
## 18 49642.10 51175.10 21139.80 21922.60 21552.70 40946.90 38707.40
## 19 42596.20 42046.80 19525.40 19315.40 19958.50 59180.90 35994.70
## 20 48432.30 49956.10 56283.50 56704.30 58544.10 54070.70 73954.60
## 21 32147.60 32373.60 56444.90 60543.50 66690.70 86707.50 85720.50
## 22 68086.00 72914.80 48508.50 48602.60 51058.20 68051.40 67947.20
## 23 46249.70 45416.00 41998.40 40154.90 39674.30 27985.80 36569.90
## 24 60732.90 58532.10 46767.50 46138.00 41185.20 35397.80 35651.30
## 25 30127.70 31161.50 39913.10 41775.90 49642.30 41559.30 42222.90
## 26 16789.30 17244.80 30263.10 28110.70 31344.10 33183.40 33157.60
## 27 43647.20 41969.20 56849.20 53925.50 57143.10 22600.30 21981.20
## 28 30157.30 30522.90 24578.60 24211.20 20018.80 26718.90 26559.50
## 29 66664.10 68362.70 29634.10 30393.20 30670.50 47217.00 48606.80
## 30 45949.00 45763.60 58566.70 59261.60 59506.10 50092.90 49924.20
## 31 39524.30 37177.00 14482.40 14134.80 15217.70 32227.90 32520.70
## 32 38483.70 39766.30 31235.20 30696.30 30225.60 31577.30 19806.60
## 33 79033.30 64595.20 56348.00 49747.90 47221.80 47054.30 59811.90
## 34 82476.30 83972.40 80268.20 51710.40 51334.00 44163.60 27575.80
## 35 35365.50 25923.30 25757.10 22528.80 22554.70 22839.70 30600.90
## 36 85748.90 50835.30 19568.60 38347.40 38800.70 37505.10 33733.50
## 37 42442.70 42512.50 39710.60 26259.30 28212.50 44065.80 43773.70
## 38 62123.40 62268.50 49200.30 49381.10 49982.70 25729.40 31905.50
## 39 18307.40 18242.10 24016.40 24047.70 26768.80 54454.00 57619.60
## 40 64880.00 64752.50 67484.60 54697.70 51167.50 54359.70 65889.90
## 41 67162.80 59464.80 57604.50 66211.20 62673.80 61867.60 51786.70
## 42 26136.10 25005.00 25811.70 92230.70 77126.70 74819.70 27225.70
## 43 30190.70 29806.40 29122.90 37257.90 36209.70 37008.70 51983.90
## 44 43367.20 43368.90 44344.10 38387.30 37200.80 36492.30 65271.00
## 45 46268.70 41265.30 41293.20 38397.30 38660.00 39234.30 20321.20
## 46 29103.40 30314.20 27900.80 26007.00 26280.20 25795.00 17042.60
## 47 28827.40 25594.80 28154.70 21310.20 20543.70 21533.00 38503.10
## 48 53126.70 51363.70 55342.50 25930.80 32272.30 42464.00 43468.70
## 49 43466.10 46518.20 49366.60 45600.20 42968.60 40492.00 69208.90
## 50 26796.50 26827.30 14648.40 15195.00 15228.60 21971.70 21490.90
## 51 26470.00 24553.30 44747.90 49387.40 48682.70 32729.20 32943.40
## 52 45542.00 42869.50 11059.60 11072.30 11212.20 21882.70 12015.20
## 53 19264.00 22804.30 34176.10 34742.00 32064.20 13905.60 10426.90
## 54 21768.00 21547.10 33151.40 33938.50 38018.60 70888.00 73787.30
## 55 36982.80 36792.70 57046.00 53723.70 65013.40 62761.20 62109.00
## 56 35787.20 35529.60 65044.00 65894.00 74213.20 31753.60 32532.30
## 57 34415.00 34691.00 50347.80 49188.90 48641.80 51192.40 53159.90
## 58 21009.70 21498.50 38563.20 35604.70 35665.80 43280.60 45208.90
## 59 19352.10 18335.70 102656.00 103134.00 107421.00 45005.90 45362.30
## 60 33014.30 31277.70 40209.10 39084.60 38954.80 25939.30 24467.50
## 61 25725.70 25826.40 47592.00 46210.10 46588.00 47614.20 47846.50
## 62 42398.20 21068.90 18595.70 19171.30 24029.20 35036.60 34086.10
## 63 19903.70 25548.10 26980.90 27590.40 34697.00 35075.00 40466.50
## 64 88881.90 105870.00 84971.80 56030.80 56325.10 60990.10 69021.30
## 65 115731.00 91785.80 76672.80 59849.00 55267.50 61637.50 69468.30
## 66 35795.40 35841.00 36879.40 64251.40 62439.30 67023.60 33218.60
## 67 44686.80 42973.60 43950.50 36304.40 35606.40 35915.40 19143.70
## 68 46022.90 43451.10 44346.90 14622.20 14961.90 15210.90 64311.70
## 69 23806.50 24378.70 24877.40 40529.50 40698.60 43037.80 25475.30
## 70 25873.70 26792.90 23814.00 108332.00 108457.00 109827.00 18816.30
## 71 31979.10 34270.80 34602.70 36063.00 36141.60 44883.90 45434.70
## 72 65138.90 66296.50 67523.00 26827.70 22778.00 24180.00 71072.40
## 73 23796.50 22617.60 21524.70 36175.00 35412.70 36235.20 20882.30
## 74 23592.40 25132.30 25466.30 54216.80 57300.50 53880.40 23382.60
## 75 19964.40 18098.20 20473.70 32108.50 32213.90 40660.90 36524.70
## 76 23240.80 17214.50 11430.40 37002.30 37192.20 43438.60 29193.90
## 77 38533.50 33103.50 33018.60 102568.00 101909.00 90391.60 60592.20
## 78 54529.60 68846.80 75470.70 25201.50 25433.10 25754.90 116304.00
## 79 27215.50 26348.50 26109.90 66030.60 64068.10 65149.90 52043.90
## 80 30929.00 31230.30 31535.40 58829.80 58854.00 60918.40 42967.90
## 81 52846.40 56571.80 56698.90 48165.70 49668.00 48650.40 43571.10
## 82 41945.00 46451.90 53564.80 58427.60 59765.20 61420.50 43932.00
## 83 46911.60 45984.10 49532.10 23316.50 22575.70 23059.60 19334.30
## 84 20755.20 22504.50 22767.10 20898.10 30135.30 31914.70 46733.50
## 85 26332.70 25787.40 29351.40 19557.10 10095.80 10223.40 38373.70
## 86 24017.50 24225.70 24241.90 78925.60 75475.80 80247.40 37481.80
## 87 98192.20 96341.90 98169.70 30991.60 30222.80 32136.70 28506.40
## 88 23175.20 25156.10 28633.30 42423.20 44909.50 45739.20 65298.20
## 89 159392.00 162641.00 165391.00 31992.00 31839.80 36043.30 41738.20
## 90 51625.50 52904.50 53600.10 53477.30 53574.50 53449.80 49772.90
## 91 57749.50 57601.80 57953.70 42102.90 35338.10 31894.90 39546.70
## 92 23639.90 23872.50 23939.00 18790.20 17723.20 18480.90 21967.30
## 93 47434.80 49620.90 47950.30 41401.90 40895.10 41663.20 62445.80
## 94 37115.20 36939.00 38090.30 31388.90 26236.00 32608.80 49455.20
## 95 63065.10 77160.00 74678.80 52935.40 54219.80 61742.80 26323.50
## 96 65232.60 62492.90 63073.60 71460.60 67648.80 72656.80 48270.10
## 97 20773.60 20797.50 21060.20 51520.40 53331.20 57948.00 25755.90
## 98 43350.40 36886.30 39175.90 30957.00 24520.70 26139.10 61454.90
## 99 34404.40 35392.40 37129.20 19448.90 21644.50 19027.20 52686.30
## 100 26621.90 23380.90 24874.40 36559.60 42329.90 39826.40 36867.80
## 101 49126.00 50587.70 53688.10 62703.80 63910.70 66644.70 27604.60
## 102 35292.00 34937.20 35294.40 66471.30 69643.00 72220.40 47392.30
## 103 20890.00 21389.60 22031.60 18685.60 23297.70 25503.60 73089.20
## 104 8337.42 19315.70 24182.10 10075.90 56913.10 55689.10 56493.40
## 105 42655.20 73418.10 70718.10 71368.60 43021.80 41589.80 41181.60
## 106 39999.30 33971.70 35268.20 36676.30 27640.20 26992.10 25825.50
## 107 44506.10 44249.80 44316.00 38018.60 42230.80 40611.80 44978.80
## 108 27354.60 28720.00 29191.10 42897.90 45091.60 40793.10 36347.70
## 109 20973.70 21498.60 22504.20 51975.20 49255.70 53811.40 46056.40
## 110 26495.00 25808.60 25231.50 33252.40 36906.30 32586.00 27694.10
## 111 32824.10 31240.20 27320.10 26247.00 66966.40 66335.10 67948.40
## 112 24000.20 96525.50 94786.60 85413.40 35360.50 30450.00 37793.90
## 113 37058.50 28033.70 25370.70 28884.60 65047.80 64813.20 61534.40
## 114 82072.90 27128.60 25399.10 23029.10 21497.60 21669.90 22742.30
## 115 47105.20 29327.90 27159.60 28285.40 22150.80 24043.70 25469.70
## 116 20763.40 32879.60 33181.10 34022.40 46394.70 45873.90 45781.60
## Aug Sep Oct Nov Dec
## 1 127523.00 128534.00 31223.40 33370.20 36439.60
## 2 54962.60 55028.40 83664.60 78976.00 84087.90
## 3 45328.70 46605.20 33219.20 36176.50 35251.20
## 4 54758.00 71981.80 65598.40 43861.10 44481.30
## 5 30139.00 31073.30 31448.70 33105.90 48879.90
## 6 109712.00 105819.00 45350.90 46966.60 38996.70
## 7 37365.50 37426.70 68732.10 50789.70 50651.90
## 8 27695.60 28458.80 32432.10 33178.20 45162.80
## 9 39944.20 39492.30 24091.30 23443.40 24135.80
## 10 34418.10 35284.10 31778.70 32064.00 32984.30
## 11 198142.00 200646.00 36745.80 36745.20 37645.00
## 12 51008.60 65350.60 79621.50 73783.00 28905.00
## 13 51215.50 23718.40 23831.40 24716.00 37122.20
## 14 36835.10 32947.30 31594.00 30774.80 20562.00
## 15 28829.30 29168.90 28188.00 30208.60 79828.50
## 16 43921.10 71998.50 69198.40 69581.20 105175.00
## 17 47785.50 68442.60 69943.50 71937.80 50403.00
## 18 37939.10 46136.60 46479.80 46874.10 21996.70
## 19 30185.40 72441.00 71827.30 71994.90 46794.00
## 20 132464.00 89686.10 88448.00 86841.20 39206.40
## 21 84761.70 33633.50 33239.50 34427.80 68039.40
## 22 66647.10 42875.40 38836.20 36705.70 47365.60
## 23 37206.30 27723.70 29854.10 33108.80 64519.40
## 24 31575.30 21261.80 20753.90 21132.00 29929.10
## 25 42147.90 30875.60 31284.10 31681.70 17206.60
## 26 33298.90 63359.80 60697.00 58633.50 43191.80
## 27 23820.50 27276.50 27416.80 28150.10 30404.70
## 28 26253.10 30481.80 29974.60 30044.90 63856.70
## 29 52128.30 145786.00 153984.00 157053.00 45177.80
## 30 50093.10 40905.50 43854.50 44429.00 41974.20
## 31 32828.80 24027.70 24394.70 28566.10 37826.90
## 32 18501.80 18463.50 11092.70 11105.40 11245.80
## 33 59807.80 59015.80 56298.70 41058.50 29181.80
## 34 27722.20 47435.90 23891.90 24711.40 26093.60
## 35 26359.70 26630.10 44033.70 43388.80 43366.10
## 36 32303.80 32626.20 48646.10 54293.70 42538.50
## 37 44747.40 38568.30 35362.00 32332.50 64518.90
## 38 30293.60 33400.80 20427.00 41465.30 18837.30
## 39 52386.20 24383.60 31660.20 31367.40 30636.90
## 40 68109.60 69704.80 57784.00 63806.10 68128.30
## 41 50318.10 46779.70 53132.30 52889.40 57329.20
## 42 25615.40 25918.40 53675.70 54251.90 62297.70
## 43 53697.60 55863.70 58332.70 57048.90 59867.40
## 44 63217.50 63771.40 47183.40 45294.90 40453.00
## 45 19867.40 21056.60 41736.90 43956.70 46272.70
## 46 20981.50 18703.40 19339.10 19964.20 19860.10
## 47 39001.30 39882.20 12645.00 11841.20 11962.40
## 48 44579.60 21120.60 27416.50 27959.90 26786.60
## 49 67159.50 66642.50 78752.80 67762.30 71304.50
## 50 20983.10 24914.10 25246.60 25257.80 27304.00
## 51 33363.20 24919.40 25367.50 25180.40 38381.50
## 52 12167.00 21338.00 20843.00 21109.00 19555.10
## 53 10608.90 18819.20 17686.20 17273.50 21427.30
## 54 76079.90 53471.80 53608.90 54765.30 33768.80
## 55 62595.40 32928.00 32840.30 33164.20 37225.90
## 56 32427.10 30521.20 31594.80 31214.70 35159.90
## 57 54865.70 30031.90 31265.30 31616.40 20464.80
## 58 42926.90 22926.00 23363.00 22743.40 19565.10
## 59 45901.80 24906.50 24255.60 25049.00 30919.30
## 60 24596.10 38859.10 38269.20 40761.10 25840.20
## 61 46614.70 10012.60 10139.20 40410.30 39999.20
## 62 24326.40 23842.90 27172.40 19319.90 19528.80
## 63 43946.30 43996.90 69354.70 68956.50 68223.50
## 64 69231.70 71384.60 28728.10 30565.80 30637.10
## 65 70796.10 71221.40 27118.70 27429.00 28153.00
## 66 31268.60 32308.20 32582.30 31183.30 33116.70
## 67 23097.40 24249.20 68566.70 77740.30 61215.70
## 68 129541.00 125226.00 16481.60 16812.80 16787.90
## 69 26835.80 26563.60 78392.40 82557.00 84283.80
## 70 19663.70 19730.20 42707.20 43057.90 42494.10
## 71 45642.70 45586.40 39725.50 41078.30 43935.80
## 72 65314.90 64494.40 39492.60 36278.70 34941.90
## 73 21786.30 22704.30 28922.10 30629.70 31036.10
## 74 19884.80 19480.00 43489.70 40485.80 35150.60
## 75 35615.70 13369.50 31334.50 31960.50 31211.30
## 76 20191.70 20446.80 19361.40 15882.00 15148.30
## 77 61850.60 65731.90 39191.10 37757.80 38211.30
## 78 116161.00 115829.00 96544.30 93605.40 92550.60
## 79 47376.70 48756.60 31493.70 31399.10 31432.20
## 80 43760.20 42158.40 86990.80 87685.40 88307.20
## 81 41979.60 42307.60 54842.60 55151.60 57247.60
## 82 43357.10 45490.60 39746.20 40028.20 40443.70
## 83 20872.40 20987.90 27289.40 32750.90 35674.30
## 84 47007.40 46877.40 18473.10 22007.00 33405.20
## 85 37999.60 38317.80 21763.70 24662.00 23108.70
## 86 30981.80 32819.30 122884.00 123026.00 124580.00
## 87 28944.50 29575.10 30794.30 30791.90 30074.40
## 88 64321.00 66105.20 62565.90 65447.20 73786.60
## 89 40317.00 30624.90 71371.50 75548.70 88551.30
## 90 49603.80 50585.70 47079.40 48046.00 48528.40
## 91 36751.30 36309.40 26414.70 26455.80 25685.40
## 92 22415.50 23524.00 24412.10 22233.20 22585.50
## 93 60724.50 64098.00 56998.00 61947.30 62831.00
## 94 49745.70 50495.30 60043.40 59518.80 60540.80
## 95 25911.80 30745.40 46430.30 46531.80 47564.90
## 96 43430.30 52244.40 59055.00 56869.60 54692.90
## 97 25753.30 25808.10 28741.30 30894.70 30298.10
## 98 60501.50 60642.90 30114.50 33036.20 34005.20
## 99 52162.30 53350.60 31410.50 30901.20 30758.80
## 100 34404.40 46513.90 189149.00 118336.00 112827.00
## 101 16322.10 13392.30 46230.60 45617.50 46265.60
## 102 43807.30 41330.30 29153.50 29424.70 29677.10
## 103 63339.90 47373.40 28652.40 27843.20 27690.10
## 104 34210.80 35481.30 38829.90 42280.20 42157.40
## 105 60677.80 61860.70 60623.30 48823.60 40484.70
## 106 20025.30 20278.30 35841.30 35882.50 36335.90
## 107 47913.00 62237.30 28028.00 28053.10 28614.70
## 108 37346.20 36901.50 24029.60 24677.10 25195.80
## 109 47831.90 41292.50 24739.00 24680.80 23879.80
## 110 28144.90 28366.40 26321.90 27099.90 23105.10
## 111 52370.60 52892.70 52604.60 28917.50 23506.70
## 112 56818.20 55409.00 51108.00 37084.80 36251.20
## 113 20587.60 19530.00 12707.00 86300.00 81814.50
## 114 51009.60 46019.80 43031.20 50820.70 44322.00
## 115 44678.60 43028.50 41019.40 20480.70 20504.30
## 116ddata3 <- decompose(tsdata3)
plot(ddata3)
plot(ddata3$seasonal)
plot(ddata3$trend)
plot(fsal$avg_wage_ft,type ="l",main = " Female Average salary",xlab= "Year",ylab="Average Salary")
#class
class(tsdata3)## [1] "ts"arima model
mymodel3 <- auto.arima(tsdata3)
mymodel3## Series: tsdata3
## ARIMA(1,0,3)(1,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 ma2 ma3 sar1 mean
## 0.9026 -0.0696 -0.1209 -0.4726 -0.0365 43185.427
## s.e. 0.0397 0.0482 0.0423 0.0356 0.0276 1406.228
##
## sigma^2 estimated as 248663125: log likelihood=-15372.11
## AIC=30758.22 AICc=30758.3 BIC=30794.86fitting model
auto.arima(tsdata3,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 30742.64
## ARIMA(0,0,0) with non-zero mean : 31782.99
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 30909.8
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 31195
## ARIMA(0,0,0) with zero mean : 33887.97
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 30769.33
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 30741.01
## ARIMA(2,0,2) with non-zero mean : 30768.15
## ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 30744.25
## ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 30742.41
## ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 30741
## ARIMA(1,0,2) with non-zero mean : 30767.52
## ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 30744.23
## ARIMA(1,0,2)(1,0,1)[12] with non-zero mean : 30742.65
## ARIMA(1,0,2)(0,0,1)[12] with non-zero mean : 30768.92
## ARIMA(1,0,2)(2,0,1)[12] with non-zero mean : 30742.88
## ARIMA(0,0,2)(1,0,0)[12] with non-zero mean : 30741.56
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 30886.4
## ARIMA(1,0,3)(1,0,0)[12] with non-zero mean : 30728.68
## ARIMA(1,0,3) with non-zero mean : 30757.41
## ARIMA(1,0,3)(2,0,0)[12] with non-zero mean : 30733.8
## ARIMA(1,0,3)(1,0,1)[12] with non-zero mean : 30730.29
## ARIMA(1,0,3)(0,0,1)[12] with non-zero mean : 30757.52
## ARIMA(1,0,3)(2,0,1)[12] with non-zero mean : 30731.33
## ARIMA(0,0,3)(1,0,0)[12] with non-zero mean : 30741.16
## ARIMA(2,0,3)(1,0,0)[12] with non-zero mean : 30731.32
## ARIMA(1,0,4)(1,0,0)[12] with non-zero mean : 30730.3
## ARIMA(0,0,4)(1,0,0)[12] with non-zero mean : 30740.4
## ARIMA(2,0,4)(1,0,0)[12] with non-zero mean : 30733.43
## ARIMA(1,0,3)(1,0,0)[12] with zero mean : Inf
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,0,3)(1,0,0)[12] with non-zero mean : 30758.22
##
## Best model: ARIMA(1,0,3)(1,0,0)[12] with non-zero mean## Series: tsdata3
## ARIMA(1,0,3)(1,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 ma2 ma3 sar1 mean
## 0.9026 -0.0696 -0.1209 -0.4726 -0.0365 43185.427
## s.e. 0.0397 0.0482 0.0423 0.0356 0.0276 1406.228
##
## sigma^2 estimated as 248663125: log likelihood=-15372.11
## AIC=30758.22 AICc=30758.3 BIC=30794.86Residuals
library(tseries)
adf.test(mymodel3$residuals)## Warning in adf.test(mymodel3$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel3$residuals
## Dickey-Fuller = -9.9963, Lag order = 11, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel3$residuals)
acf(ts(mymodel3$residuals),main = "ACF Residual")
pacf(ts(mymodel3$residuals),main = "PACF Residual")
#forecasting female salary and mean
myforecast3 <- forecast(mymodel3, level =c (95), h= 3*12)
plot(myforecast3,main = " Female Salary forecasting for the next 3years",xlab = "Time", ylab ="Average salary")
myforecast3[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 116
## 117 42675.15 42362.16 42468.03 42542.80 42186.18 42291.13 42372.07
## 118 42815.60 42864.88 42895.17 42923.27 42964.12 42985.41 43005.12
## 119 43085.40 43094.66 43103.54 43111.52 43118.16 43124.73 43130.63
## Aug Sep Oct Nov Dec
## 116 39983.05 41174.03 41456.86 42383.04 42541.14
## 117 42653.84 42673.53 42720.23 42737.88 42778.56
## 118 43015.29 43033.04 43048.00 43062.40 43074.50
## 119calculating average women salary
avgsal<- 53392.66/43074.50
avgsal## [1] 1.239542cat("The average mens salary is ",avgsal,"times of womens salary")## The average mens salary is 1.239542 times of womens salarycalculating how much women earns for every men who earns a dollar
cents <- 1/avgsal
cents## [1] 0.8067495cat("The women earns",cents*100, "cents for every men who earns a dollar")## The women earns 80.67495 cents for every men who earns a dollarforecasting for all races: dataset 2
library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
allrace <- subset(newrace, select = c(avg_wage, year))
head(allrace)allrace$avg_wage <- as.numeric(allrace$avg_wage)
allrace$Date <- paste (allrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata4 <- ts(allrace$avg_wage,frequency = 12)
tsdata4## Jan Feb Mar Apr May
## 1 66809.0000 48178.6000 44442.7000 57379.2000 52914.1000
## 2 160712.0000 99637.8000 41850.1000 50330.3000 19293.5000
## 3 51690.6000 80406.1000 47527.0000 161415.0000 37612.2000
## 4 44766.0000 93734.2000 70695.0000 84248.5000 56351.9000
## 5 31928.0000 39364.5000 86738.5000 8654.8500 59888.0000
## 6 122884.0000 37274.9000 87813.2000 118473.0000 49086.0000
## 7 32824.1000 82837.4000 131296.0000 41933.2000 72926.1000
## 8 32023.5000 26625.0000 45953.7000 30002.7000 5689.5100
## 9 32155.8000 80820.6000 54561.0000 58975.5000 58794.7000
## 10 50983.0000 117975.0000 56568.7000 108226.0000 51201.9000
## 11 1466.7400 46748.3000 27099.0000 43154.8000 13504.2000
## 12 28264.3000 32600.9000 91407.8000 93783.5000 41939.4000
## 13 14201.3000 8529.9300 17485.3000 36797.4000 28856.0000
## 14 31060.4000 26057.3000 27017.0000 32155.8000 25774.3000
## 15 17673.2000 45018.2000 13070.1000 22101.2000 25114.5000
## 16 41063.1000 22047.0000 22133.8000 10289.9000 24490.3000
## 17 8665.7400 24499.9000 23667.2000 25724.7000 18151.7000
## 18 40337.0000 31450.0000 25819.2000 30693.7000 29707.0000
## 19 55755.3000 46985.7000 23193.8000 53355.0000 41666.2000
## 20 202415.0000 32432.7000 51243.7000 65648.2000 66562.4000
## 21 32997.5000 28201.2000 32892.9000 21882.7000 64311.7000
## 22 48242.5000 32813.8000 49236.1000 43009.6000 70699.5000
## 23 33660.9000 39696.7000 24633.2000 28572.5000 12684.3000
## 24 57543.2000 42729.0000 54186.6000 28061.4000 25926.2000
## 25 30049.6000 28036.2000 43135.9000 44282.0000 31726.6000
## 26 22439.5000 52518.5000 26042.5000 21799.3000 27202.7000
## 27 12393.9000 10302.8000 17997.6000 18073.9000 14145.3000
## 28 21054.6000 40469.7000 28939.0000 34373.8000 26414.3000
## 29 17920.6000 44362.4000 46312.0000 67707.5000 50330.3000
## 30 126053.0000 125508.0000 7373.0700 74392.3000 96135.4000
## 31 83792.4000 33228.7000 63540.9000 61436.8000 20168.5000
## 32 82816.1000 54893.7000 43300.7000 37499.5000 53821.1000
## 33 24289.2000 10240.4000 20085.2000 19307.4000 43785.6000
## 34 39021.7000 47757.3000 49911.0000 99108.5000 41217.6000
## 35 10433.5000 8798.6400 22490.6000 8682.0700 12478.5000
## 36 97593.5000 53593.1000 51115.8000 42219.4000 95832.2000
## 37 92056.7000 44054.5000 79293.7000 180532.0000 72886.5000
## 38 77374.7000 40411.7000 37210.9000 47276.8000 44185.0000
## 39 132140.0000 82468.5000 88718.0000 64582.5000 89303.1000
## 40 75928.9000 67288.0000 98891.7000 70750.4000 72261.7000
## 41 42826.0000 76178.3000 59585.7000 61442.2000 75588.8000
## 42 50600.6000 43333.3000 28130.1000 64392.6000 49667.7000
## 43 22326.0000 30581.5000 35311.4000 27489.2000 23558.9000
## 44 55211.3000 35012.4000 68379.9000 88540.6000 65810.2000
## 45 26796.5000 17572.5000 17727.4000 14091.0000 11187.7000
## 46 19539.2000 43753.1000 62135.0000 41609.2000 75902.7000
## 47 7291.5800 35629.6000 36389.5000 27029.6000 22088.1000
## 48 8710.2200 35825.9000 34381.4000 10634.2000 33008.7000
## 49 49701.9000 43100.8000 36457.9000 30252.8000 69671.0000
## 50 65387.8000 51838.6000 51648.9000 72486.5000 57271.7000
## 51 26071.1000 26219.1000 30012.1000 26461.4000 36987.8000
## 52 21259.7000 22423.4000 21704.3000 22873.6000 16412.0000
## 53 27451.6000 22981.2000 18252.9000 22985.1000 77953.6000
## 54 37515.1000 27079.0000 24522.8000 21959.9000 42574.4000
## 55 137452.0000 136167.0000 121638.0000 50598.4000 71564.1000
## 56 70032.4000 39388.9000 82975.2000 77868.1000 76376.7000
## 57 29488.4000 13129.6000 38228.5000 34660.8000 29613.9000
## 58 1310.9500 10249.7000 13231.4000 16821.7000 12127.1000
## 59 21819.0000 19338.2000 25141.3000 13129.6000 4006.7500
## 60 39549.8000 30493.8000 5104.1000 34424.1000 34236.1000
## 61 23808.9000 22675.7000 14967.0000 14971.5000 37676.5000
## 62 49153.8000 49350.8000 33419.4000 40961.5000 35520.8000
## 63 58261.5000 35975.4000 36657.5000 26684.1000 26689.3000
## 64 30413.9000 37515.1000 60868.5000 39143.3000 74366.9000
## 65 15683.3000 39348.1000 36712.9000 58089.0000 37305.5000
## 66 28437.2000 36073.8000 22692.5000 42254.6000 54425.2000
## 67 37750.5000 37646.6000 72093.7000 56603.6000 85748.9000
## 68 12101.1000 60550.0000 43765.4000 30721.1000 33853.8000
## 69 39981.0000 14661.4000 53007.4000 50559.8000 42353.9000
## 70 17248.2000 71670.9000 56102.0000 89440.6000 27353.4000
## 71 32167.6000 43032.9000 42030.5000 20975.2000 12895.7000
## 72 22831.4000 17143.2000 56124.5000 42865.3000 28986.0000
## 73 64179.3000 5173.6000 24125.0000 20243.6000 26055.6000
## 74 46365.6000 124233.0000 83879.9000 55608.1000 67295.9000
## 75 59113.1000 10941.4000 94479.7000 115978.0000 59388.2000
## 76 52192.2000 42511.8000 35355.3000 52082.7000 39304.4000
## 77 64227.8000 34759.4000 40995.3000 26109.8000 6144.2200
## 78 20480.7000 46352.5000 22788.0000 20951.1000 55818.5000
## 79 27099.3000 32291.3000 30906.3000 36307.3000 36941.6000
## 80 52888.2000 48248.7000 44471.5000 54725.2000 45403.4000
## 81 69744.9000 46606.4000 40054.6000 23713.9000 45025.2000
## 82 27364.0000 36486.2000 74828.5000 58405.1000 55463.4000
## 83 53593.1000 41930.5000 38029.4000 54452.7000 49156.2000
## 84 31323.2000 42736.0000 36063.4000 35806.5000 39716.1000
## 85 71814.7000 42638.4000 39105.4000 36554.2000 38062.3000
## 86 19584.8000 12143.2000 33332.9000 13076.2000 14265.4000
## 87 30757.0000 27078.0000 19650.7000 28528.8000 16775.7000
## 88 17178.7000 31817.8000 46193.3000 29170.3000 38266.0000
## 89 47665.1000 12288.4000 41947.4000 10507.0000 39492.0000
## 90 26219.1000 86680.2000 24148.2000 55736.8000 99331.6000
## 91 69852.5000 62286.2000 51201.9000 69763.1000 61442.2000
## 92 16525.2000 19896.3000 16412.0000 28143.0000 14964.5000
## 93 21221.2000 23362.5000 19683.4000 25799.2000 22101.0000
## 94 47018.2000 6249.9200 31536.7000 30035.1000 26466.5000
## 95 12035.5000 25712.2000 22276.3000 25884.0000 13943.9000
## 96 42185.0000 40284.5000 49211.1000 29938.4000 35956.5000
## 97 7717.4000 28217.2000 16318.5000 16651.8000 17050.1000
## 98 47555.9000 26939.8000 32326.8000 36261.4000 21437.2000
## 99 10145.7000 18601.5000 19983.5000 3829.4800 11189.1000
## 100 15865.0000 40476.8000 75589.9000 58806.6000 79424.1000
## 101 50648.5000 45981.2000 50048.5000 44203.3000 38047.1000
## 102 71540.1000 57202.2000 79917.0000 74405.1000 66713.0000
## 103 31553.9000 45350.2000 45018.2000 36304.8000 30246.3000
## 104 79270.1000 57878.4000 133983.0000 78052.4000 43946.3000
## 105 38315.4000 43319.4000 29859.7000 23247.1000 26259.3000
## 106 32641.9000 33091.0000 63982.9000 46366.5000 55923.1000
## 107 42510.2000 25018.2000 27525.3000 24576.9000 41159.1000
## 108 17173.3000 18345.5000 20476.3000 28954.6000 24147.2000
## 109 20565.1000 21327.6000 15638.7000 23202.4000 22796.7000
## 110 5647.1800 19774.1000 12961.8000 135886.0000 86012.1000
## 111 42671.3000 31438.8000 34689.9000 20113.5000 20299.3000
## 112 8360.5200 5405.7700 37489.2000 37774.7000 32603.8000
## 113 27227.5000 7398.7700 9075.8300 28132.9000 35094.9000
## 114 38421.0000 23962.5000 35416.2000 61987.9000 66225.0000
## 115 38184.0000 48129.7000 28742.1000 18958.1000 55895.3000
## 116 23289.4000 10035.6000 23580.9000 29595.8000 30558.7000
## 117 17353.5000 36561.5000 25600.9000 20812.3000 17706.1000
## 118 12115.2000 24600.7000 36729.3000 31053.6000 31113.1000
## 119 82116.0000 49846.1000 157564.0000 35992.4000 75341.8000
## 120 61853.5000 37652.4000 41666.2000 57994.8000 53241.9000
## 121 25030.4000 27449.4000 14189.1000 30252.8000 22052.2000
## 122 42707.8000 59250.7000 76597.6000 62656.8000 81844.1000
## 123 27017.8000 20030.4000 29803.8000 25060.5000 30459.3000
## 124 47865.6000 73688.0000 62835.6000 72118.7000 28389.6000
## 125 32863.6000 31257.8000 29559.6000 41366.7000 28198.9000
## 126 37322.5000 26645.8000 32824.1000 40763.9000 27974.5000
## 127 82654.7000 73144.6000 76589.5000 48957.7000 109353.0000
## 128 46188.0000 41963.8000 40859.5000 48614.0000 14239.5000
## 129 22647.9000 15120.3000 22170.1000 27038.0000 24899.1000
## 130 26309.9000 41101.0000 14643.7000 17683.7000 29327.9000
## 131 71179.0000 92875.6000 73623.1000 16129.3000 17984.9000
## 132 63709.8000 161348.0000 12004.3000 18033.0000 11017.8000
## 133 50965.3000 33379.8000 24443.5000 32753.9000 32093.2000
## 134 5949.7100 48236.4000 40547.9000 26420.2000 36212.1000
## 135 21261.4000 28525.8000 23962.1000 81584.9000 60614.2000
## 136 39343.2000 36874.6000 20474.2000 24478.9000 70491.0000
## 137 30148.0000 34183.5000 31797.4000 27698.5000 17783.1000
## 138 30852.6000 19747.4000 20305.0000 38587.0000 18711.6000
## 139 13714.6000 9374.8900 28429.0000 32400.5000 30416.4000
## 140 48940.8000 252377.0000 21566.9000 20228.5000 1354.1500
## 141 416.6620 20275.9000 22024.9000 35208.3000 25737.9000
## 142 41337.8000 18993.5000 9371.3900 2048.0700 5042.1200
## 143 24523.0000 24051.1000 12158.9000 16697.8000 14802.0000
## 144 13315.4000 99752.8000 56419.5000 184327.0000 95321.4000
## 145 39557.8000 11790.5000 36303.3000 60989.8000 30613.1000
## 146 4299.2300 12869.6000 14438.9000 143760.0000 148458.0000
## 147 158973.0000 26439.1000 23749.8000 23022.5000 24713.8000
## 148 85748.9000 45283.0000 65547.6000 27269.6000 27321.5000
## 149 22492.5000 16077.9000 42748.1000 10979.1000 20904.7000
## 150 35313.7000 25724.7000 6041.5900 44157.9000 30635.8000
## 151 43009.6000 71170.3000 67355.8000 109594.0000 57047.0000
## 152 54065.1000 34856.8000 47690.6000 35342.1000 66885.2000
## 153 45239.3000 31073.5000 21378.0000 36613.4000 54856.7000
## 154 9374.8900 40097.0000 35948.0000 32039.2000 32109.0000
## 155 44306.5000 73997.6000 36505.4000 36342.5000 22429.3000
## 156 2381.4100 20833.1000 16081.3000 16049.6000 19385.0000
## 157 24730.9000 35203.8000 25888.3000 42124.1000 25535.9000
## 158 19293.5000 48355.4000 40803.8000 18619.6000 20688.0000
## 159 16197.6000 15968.8000 35316.6000 20759.3000 32269.6000
## 160 36150.8000 33042.9000 45609.3000 35371.4000 25662.8000
## 161 18990.2000 6855.4100 20276.8000 19944.0000 14889.1000
## 162 34714.9000 20833.1000 83531.3000 45287.6000 44552.5000
## 163 145832.0000 107186.0000 98904.9000 99467.3000 88119.0000
## 164 17070.9000 12862.3000 27557.3000 29018.6000 13480.5000
## 165 19688.1000 21950.6000 19433.7000 21874.7000 51103.7000
## 166 66559.7000 55792.6000 15317.9000 70598.7000 51782.5000
## 167 201876.0000 181223.0000 52922.0000 214449.0000 129635.0000
## 168 57346.1000 30656.6000 26082.6000 25985.2000 24603.5000
## 169 36203.8000 23193.8000 24055.9000 57251.3000 61177.8000
## 170 43198.4000 52289.4000 49373.0000 44399.4000 55093.2000
## 171 54906.2000 45792.7000 36558.0000 51563.6000 84842.7000
## 172 33705.6000 39629.0000 55005.8000 38541.2000 25308.4000
## 173 18674.3000 21806.7000 20521.5000 16525.3000 25600.9000
## 174 12605.3000 28643.9000 32072.0000 29406.5000 25962.1000
## 175 44212.7000 38224.4000 25210.6000 30495.6000 62278.8000
## 176 39898.2000 51331.5000 42893.4000 38927.8000 33662.7000
## 177 35520.4000 59456.6000 35221.9000 51930.3000 62577.0000
## 178 53266.1000 58013.7000 47134.7000 96467.5000 75892.1000
## 179 17781.7000 70658.6000 43010.4000 48533.9000 56536.7000
## 180 56130.9000 56162.0000 50281.7000 40463.7000 63951.0000
## 181 71682.6000 33889.3000 55148.9000 27305.7000 15626.8000
## 182 25983.2000 20905.9000 20898.3000 24017.6000 24957.0000
## 183 20398.7000 43698.8000 22939.1000 14223.8000 22976.9000
## 184 69423.9000 52516.2000 67603.0000 56765.7000 55511.7000
## 185 27868.4000 37719.6000 41542.5000 44834.3000 11148.5000
## 186 51429.7000 56363.3000 60505.5000 53802.5000 28964.2000
## 187 19407.7000 42704.4000 41546.3000 29812.1000 396311.0000
## 188 36335.9000 58148.6000 30789.1000 39395.5000 2457.6900
## 189 37789.2000 48096.4000 156039.0000 34266.7000 71767.8000
## 190 50881.3000 43383.9000 23091.7000 51553.7000 25600.9000
## 191 39821.0000 31718.6000 30673.0000 28579.3000 28228.7000
## 192 47791.5000 56657.3000 55950.6000 89470.3000 39987.3000
## 193 34472.8000 37830.6000 29747.2000 27093.2000 23782.7000
## 194 32093.9000 35541.8000 16412.0000 33186.5000 39683.9000
## 195 26807.9000 36205.9000 15541.8000 27857.7000 18432.7000
## 196 37499.5000 29345.2000 15591.1000 48194.3000 66003.8000
## 197 196615.0000 50494.2000 8089.8900 34453.4000 42171.1000
## 198 84351.8000 73941.9000 110785.0000 77822.8000 60418.2000
## 199 44077.0000 35359.8000 57276.7000 50042.5000 57024.3000
## 200 38615.8000 37431.8000 17223.8000 82060.2000 72541.7000
## 201 21015.0000 18216.4000 33890.3000 29797.3000 30955.4000
## 202 20611.6000 3455.3400 49165.0000 44038.0000 36012.4000
## 203 21580.3000 46833.4000 29301.9000 93001.6000 25654.9000
## 204 25165.1000 54200.7000 53187.1000 48233.7000 31660.0000
## 205 27393.4000 22854.5000 22314.1000 26435.2000 36208.6000
## 206 18045.4000 16841.9000 57922.2000 32739.9000 26802.8000
## 207 16474.5000 23817.0000 12971.2000 17392.0000 24749.4000
## 208 41483.5000 17042.6000 45299.8000 23464.0000 26325.2000
## 209 35757.1000 22291.4000 42783.2000 57049.6000 26796.5000
## 210 17668.9000 78528.7000 67347.8000 70832.5000 75417.4000
## 211 56534.1000 33108.9000 25202.9000 24783.9000 12862.3000
## 212 35408.1000 44394.6000 36493.0000 29186.9000 49903.2000
## 213 36608.9000 34379.0000 33060.9000 38841.8000 41879.3000
## 214 72433.3000 68374.8000 23228.9000 53937.0000 80966.1000
## 215 58626.1000 35534.9000 22352.5000 30399.1000 32155.8000
## 216 16946.3000 47928.3000 42124.2000 53057.1000 57195.8000
## 217 28607.9000 30261.4000 22185.3000 30012.1000 31976.7000
## 218 29285.7000 31133.6000 31119.6000 19059.3000 40731.3000
## 219 31008.7000 25600.9000 28050.2000 25673.9000 10072.3000
## 220 51799.4000 33270.2000 88959.2000 64453.7000 61796.3000
## 221 82941.0000 126198.0000 124900.0000 7381.5500 82588.4000
## 222 58742.0000 57606.0000 84397.4000 75481.7000 66044.6000
## 223 37015.9000 47730.1000 97743.1000 76071.6000 121260.0000
## 224 76613.8000 57061.2000 58210.3000 71645.4000 55594.9000
## 225 75390.7000 177061.0000 191568.0000 55765.8000 80558.9000
## 226 40089.1000 83197.7000 42591.3000 135571.0000 53654.7000
## 227 71461.1000 66839.2000 70913.9000 72242.6000 59565.5000
## 228 70991.5000 53058.7000 80493.2000 88642.4000 79327.9000
## 229 116643.0000 58157.1000 108377.0000 51260.8000 247168.0000
## 230 29427.8000 50682.7000 41924.4000 77578.2000 27087.1000
## 231 79202.7000 74811.0000 87010.5000 91476.0000 57174.1000
## 232 35699.4000 52624.9000 34175.3000 50085.7000 63468.5000
## 233 26190.3000 33892.7000 81803.2000 62371.9000 56568.1000
## 234 63614.0000 70088.5000 16750.6000 53305.3000 32306.7000
## 235 49091.9000 45245.3000 14763.1000 70739.8000 42832.0000
## 236 89853.1000 44836.7000 45273.8000 63263.3000 51835.5000
## 237 48747.6000 49951.9000 56102.8000 46020.2000 41775.0000
## 238 28398.4000 47059.4000 31693.4000 40565.4000 83352.5000
## 239 85796.8000 38396.8000 22401.9000 20000.8000 8839.6000
## 240 53537.6000 40700.5000 95942.4000 41818.7000 69751.1000
## 241 62020.9000 38635.1000 41714.1000 56028.4000 54904.5000
## 242 65030.4000 47484.0000 48285.8000 88920.7000 51610.7000
## 243 21461.9000 20588.1000 82179.1000 60104.6000 45860.6000
## 244 5047.9200 55936.9000 119806.0000 56122.9000 34754.8000
## 245 46102.8000 35661.0000 91589.2000 44250.0000 121172.0000
## 246 55997.9000 48289.2000 33083.5000 52878.3000 30255.4000
## 247 39702.2000 101926.0000 15378.2000 79485.1000 72970.4000
## 248 72835.7000 92066.6000 59080.4000 134137.0000 50063.2000
## 249 78446.4000 67663.7000 49367.0000 39802.2000 39666.8000
## 250 75446.2000 60487.7000 73602.1000 87895.7000 78330.9000
## 251 66154.3000 66439.2000 76253.9000 83962.3000 85293.1000
## 252 36045.5000 53408.8000 25630.4000 34967.0000 49525.6000
## 253 55335.2000 167746.0000 100666.0000 53067.0000 69528.3000
## 254 74413.9000 73783.1000 87110.0000 95351.4000 69751.1000
## 255 82286.8000 94076.4000 95417.0000 78166.6000 80101.1000
## 256 78213.9000 82948.6000 83463.3000 72590.3000 82768.7000
## 257 66438.6000 72177.3000 70047.3000 61162.5000 90704.1000
## 258 70577.5000 62584.4000 73879.3000 83822.7000 133678.0000
## 259 42756.9000 57889.4000 85941.6000 41652.3000 104300.0000
## 260 79183.8000 42155.5000 31598.8000 48060.7000 49076.7000
## 261 40360.4000 38001.9000 26378.0000 22027.8000 43278.4000
## 262 49944.8000 23220.5000 36149.3000 41714.1000 30797.3000
## 263 85399.8000 80482.0000 75939.2000 93891.4000 48492.7000
## 264 57971.4000 31789.6000 41269.9000 64071.4000 52571.1000
## 265 62686.5000 59643.7000 44930.1000 26178.7000 57978.9000
## 266 258722.0000 23355.6000 36027.7000 39043.4000 88262.5000
## 267 28299.0000 54203.4000 28168.7000 42589.6000 37832.4000
## 268 34843.5000 56181.4000 10802.6000 29128.3000 7299.9600
## 269 38728.2000 42388.5000 37643.3000 25239.6000 33476.1000
## 270 26217.9000 40621.5000 30874.9000 16706.1000 42898.7000
## 271 31170.9000 3836.2800 12514.2000 33574.0000 28216.7000
## 272 136615.0000 81991.0000 111608.0000 83699.4000 119381.0000
## 273 32957.4000 39868.5000 48271.9000 38153.3000 26450.5000
## 274 55433.6000 77483.9000 36289.2000 48285.9000 21478.6000
## 275 40433.4000 29090.3000 41595.6000 35199.8000 40233.8000
## 276 37780.8000 39817.1000 40482.7000 40064.6000 48391.3000
## 277 13752.0000 27807.9000 23860.1000 40099.2000 32280.6000
## 278 16267.0000 20003.6000 20862.7000 20343.4000 8370.1300
## 279 18869.7000 17569.1000 45681.4000 32427.6000 35335.5000
## 280 34765.8000 46647.2000 32374.2000 54507.8000 51501.6000
## 281 36252.7000 78444.2000 64385.6000 44959.2000 38249.4000
## 282 50161.2000 19539.1000 16060.6000 29812.1000 40358.4000
## 283 6257.9000 19594.6000 14814.4000 48232.3000 24236.7000
## 284 95276.9000 57356.9000 57704.4000 57856.2000 96578.5000
## 285 31416.0000 32235.2000 53424.3000 38410.6000 24272.2000
## 286 21008.8000 30997.0000 24347.5000 24685.6000 28909.9000
## 287 17145.0000 33908.7000 44002.6000 35213.4000 45066.1000
## 288 169385.0000 117159.0000 142046.0000 162850.0000 92666.0000
## 289 60205.9000 71959.4000 48060.7000 97079.9000 96484.5000
## 290 217811.0000 130404.0000 158423.0000 261509.0000 73687.5000
## 291 59327.7000 48064.8000 62003.8000 78679.0000 64958.4000
## 292 55959.7000 31915.5000 17333.6000 31239.4000 56719.5000
## 293 50739.9000 55244.7000 29278.0000 48984.4000 47882.1000
## 294 124760.0000 34279.3000 57097.5000 56217.2000 65537.1000
## 295 104039.0000 137893.0000 67918.8000 59020.2000 44452.7000
## 296 44545.2000 153588.0000 45668.5000 38471.2000 48331.4000
## 297 44995.2000 44807.1000 49239.2000 47494.3000 47882.1000
## 298 25239.6000 36447.6000 43887.5000 31931.8000 30718.3000
## 299 36106.9000 32019.4000 34866.0000 33978.6000 41008.6000
## 300 56112.7000 41999.7000 34928.0000 27699.4000 6048.5400
## 301 40545.0000 12302.6000 36106.4000 48676.5000 25559.0000
## 302 41504.5000 39204.7000 54068.3000 40184.8000 39716.1000
## 303 34875.0000 32040.4000 26349.3000 19506.1000 29532.4000
## 304 27051.3000 26965.8000 18980.2000 30044.0000 34343.4000
## 305 22005.3000 35422.8000 25138.7000 41714.1000 21060.0000
## 306 13439.9000 13841.5000 23187.3000 29418.4000 26624.2000
## 307 58399.7000 57511.4000 62493.7000 73492.3000 60633.7000
## 308 85708.7000 49834.8000 40061.3000 21250.1000 34504.0000
## 309 58848.1000 41711.3000 41369.1000 32039.4000 31656.3000
## 310 79260.1000 57072.6000 57985.0000 59507.0000 36793.5000
## 311 27561.7000 8010.1100 6509.4300 33196.3000 26655.6000
## 312 34079.3000 36088.7000 29292.5000 26839.8000 20694.7000
## 313 39811.6000 31689.9000 34327.6000 37248.7000 41051.8000
## 314 34195.4000 24973.8000 25099.7000 30831.7000 28504.9000
## 315 55522.7000 22949.4000 21908.8000 17406.8000 17276.2000
## 316 14538.4000 16352.0000 13521.9000 15436.1000 22320.4000
## 317 26586.3000 23985.8000 14849.0000 12615.9000 10744.8000
## 318 15019.0000 7989.5100 9132.7600 7753.4000 17577.7000
## 319 17985.7000 15912.6000 10194.4000 14546.1000 17961.3000
## 320 10880.4000 9200.3800 13034.6000 14110.5000 9962.7400
## 321 820.1720 17334.0000 8282.8500 36852.8000 33170.2000
## 322 17736.2000 31380.1000 29142.6000 54381.6000 15915.4000
## 323 13801.5000 20988.1000 19656.5000 17689.0000 18631.2000
## 324 28157.0000 23032.5000 22629.9000 21815.5000 19360.0000
## 325 16685.6000 31346.4000 47392.8000 20025.3000 40820.1000
## 326 35044.2000 6561.3800 19807.5000 24812.8000 12115.0000
## 327 32097.9000 8978.3100 25386.4000 12167.7000 10924.8000
## 328 10600.6000 16461.3000 13557.1000 24351.0000 11786.6000
## 329 9385.6700 7790.1400 11632.4000 23062.5000 19134.5000
## 330 38631.4000 19116.2000 23579.9000 17665.6000 18570.6000
## 331 13931.7000 5047.9200 31761.2000 32763.2000 13415.8000
## 332 20035.3000 15223.9000 37542.7000 27411.1000 1009.5900
## 333 27058.4000 23044.0000 11043.1000 15957.7000 8285.1800
## 334 15790.8000 9543.4400 47869.9000 37168.5000 32374.8000
## 335 29132.8000 24725.9000 67375.2000 19897.3000 51425.5000
## 336 16155.1000 10254.7000 27729.2000 17861.4000 20054.4000
## 337 35171.9000 28664.8000 18824.6000 28694.8000 21461.9000
## 338 26827.3000 29058.3000 100958.0000 19779.9000 46819.3000
## 339 41839.8000 103868.0000 47846.5000 69966.9000 66515.3000
## 340 62421.9000 37035.7000 43429.1000 141181.0000 39906.2000
## 341 62183.2000 45689.4000 18917.9000 10447.8000 2050.4300
## 342 44017.9000 41652.3000 48812.3000 91032.7000 77213.5000
## 343 22524.1000 24193.7000 42648.9000 10012.6000 363.4510
## 344 28895.7000 26031.2000 47287.7000 39930.6000 30522.2000
## 345 9756.7600 23830.0000 26585.2000 9067.6400 48778.9000
## 346 33076.2000 3204.0400 34307.5000 40383.4000 28886.6000
## 347 39628.4000 30539.9000 26919.9000 33075.2000 32156.9000
## 348 26249.2000 37245.7000 35371.6000 43994.6000 35412.1000
## 349 20025.3000 23317.5000 25624.7000 6174.1800 16096.4000
## 350 34795.8000 63751.3000 46552.2000 32808.7000 24030.3000
## 351 35440.1000 25800.0000 17690.6000 30037.9000 28421.1000
## 352 43817.0000 32025.6000 39736.1000 53032.3000 41714.1000
## 353 24047.1000 19188.2000 21689.4000 11719.5000 19024.0000
## 354 22787.0000 19267.3000 14495.3000 25031.6000 17144.9000
## 355 39994.1000 41967.7000 11804.0000 51310.5000 39024.3000
## 356 27559.3000 19507.8000 22098.7000 31414.0000 25085.0000
## 357 35628.4000 30501.3000 39784.3000 33991.6000 23709.8000
## 358 21324.5000 34233.0000 36983.4000 10764.8000 27546.2000
## 359 34448.6000 27785.7000 37577.2000 28043.2000 51251.3000
## 360 45698.9000 48060.7000 42003.4000 49868.6000 50059.4000
## 361 35777.0000 42927.1000 45453.7000 26352.1000 41228.7000
## 362 21781.8000 19826.4000 20575.1000 13636.1000 27342.3000
## 363 30046.6000 31774.4000 30287.6000 13424.8000 11505.3000
## 364 32840.9000 36019.9000 35012.0000 31813.0000 38281.8000
## 365 24813.8000 49061.9000 20504.3000 19600.7000 30709.8000
## 366 33617.0000 26460.0000 20653.3000 44675.7000 17070.3000
## 367 25735.8000 8510.7400 25728.4000 19640.1000 25893.4000
## 368 40020.9000 23039.8000 78530.1000 31434.1000 35205.3000
## 369 35504.5000 35329.9000 30037.9000 29420.4000 9619.7900
## 370 13123.1000 7762.0400 18081.1000 22462.7000 12506.2000
## 371 29378.9000 12302.6000 21625.6000 7631.2700 13755.0000
## 372 39077.2000 32432.2000 52142.6000 30394.7000 37141.5000
## 373 28658.9000 20306.7000 65082.2000 26688.3000 21505.1000
## 374 24494.4000 42402.6000 33222.5000 14199.7000 28149.4000
## 375 26910.4000 25054.5000 5150.8500 35947.7000 36868.2000
## 376 15193.3000 30742.7000 38402.2000 36542.2000 17117.3000
## 377 53757.8000 32612.9000 29716.3000 21783.5000 26827.3000
## 378 18801.9000 35776.1000 34332.8000 38782.4000 35720.9000
## 379 22130.9000 27013.7000 25302.9000 23220.5000 25754.3000
## 380 34098.5000 33999.1000 21962.5000 16560.7000 18010.7000
## 381 33402.5000 19865.1000 13664.9000 25202.6000 47937.2000
## 382 20025.3000 82052.7000 29704.3000 23161.8000 11160.2000
## 383 26827.3000 48337.9000 27427.7000 38185.3000 40237.8000
## 384 43658.5000 31288.2000 22027.8000 26827.3000 43574.1000
## 385 18771.3000 14896.0000 24183.1000 56457.4000 50068.9000
## 386 35674.6000 37933.0000 30517.4000 50725.7000 44608.0000
## 387 25970.0000 33824.6000 40525.9000 16690.2000 28258.5000
## 388 32954.1000 5214.2600 28469.9000 34650.9000 24422.2000
## 389 32357.8000 61360.5000 44034.2000 73263.7000 30448.9000
## 390 45295.3000 37374.3000 31566.9000 11482.4000 36023.8000
## 391 30777.3000 32192.8000 25972.9000 30565.1000 41394.3000
## 392 42656.7000 32040.4000 49267.7000 26698.2000 38751.1000
## 393 29008.8000 19341.9000 417.1410 100.1260 20299.3000
## 394 42467.7000 15017.1000 34005.9000 34240.3000 33457.5000
## 395 13306.0000 70088.5000 43445.0000 41386.7000 42581.3000
## 396 41107.1000 28706.0000 42202.2000 34968.7000 37542.7000
## 397 38585.5000 36631.4000 30275.8000 26357.9000 78213.9000
## 398 79966.8000 37168.5000 15262.8000 38465.2000 34529.8000
## 399 32617.0000 32577.7000 35595.2000 18994.2000 40121.2000
## 400 37453.8000 51550.8000 44456.7000 32277.9000 26924.5000
## 401 33445.8000 21645.4000 26381.3000 22399.3000 25630.4000
## 402 13859.9000 55262.6000 9902.5100 14724.0000 20813.6000
## 403 19531.1000 31168.0000 49210.3000 23451.0000 24703.9000
## 404 26850.5000 23103.4000 23016.9000 41272.9000 33055.7000
## 405 396767.0000 26072.0000 19530.3000 30143.0000 29844.7000
## 406 21354.6000 18779.1000 41330.5000 23111.3000 50063.2000
## 407 33229.1000 42402.6000 30405.3000 29278.0000 31828.4000
## 408 24447.4000 28460.5000 29439.6000 28369.3000 28734.5000
## 409 18284.9000 22511.7000 18193.4000 5653.6800 19308.4000
## 410 15749.5000 18729.2000 29482.6000 15608.0000 21753.0000
## 411 20349.9000 7008.8500 48507.3000 2002.5300 35501.8000
## 412 22694.2000 10299.5000 14599.9000 28882.9000 26551.7000
## 413 25358.3000 41171.2000 25630.4000 25804.8000 23481.9000
## 414 71765.0000 56140.7000 46208.9000 34380.5000 99125.1000
## 415 33717.3000 46633.4000 33574.4000 44699.4000 31877.3000
## 416 26249.2000 30046.6000 27449.6000 31468.0000 22995.8000
## 417 17162.9000 35636.0000 24255.5000 42136.1000 27237.0000
## 418 19647.3000 22027.8000 27059.5000 32433.1000 72999.6000
## 419 27930.4000 25463.9000 98939.3000 21124.3000 17121.6000
## 420 23896.3000 21899.9000 39898.6000 16902.0000 34244.8000
## 421 26827.3000 33028.3000 37709.2000 31262.9000 24886.4000
## 422 24004.0000 30651.9000 19408.1000 28800.8000 30721.1000
## 423 38886.6000 38258.4000 37046.8000 115885.0000 98093.9000
## 424 123026.0000 37673.1000 84796.3000 112642.0000 44567.4000
## 425 27604.3000 8509.2800 20710.7000 23346.4000 27774.4000
## 426 37674.5000 33369.7000 27013.7000 24998.2000 39197.5000
## 427 16209.4000 14849.1000 21351.6000 8201.7200 59373.6000
## 428 3630.2000 67635.9000 30643.8000 6653.1800 78213.9000
## 429 17940.5000 21067.3000 11927.9000 14801.5000 16273.1000
## 430 44259.4000 26837.4000 39628.4000 37483.9000 74299.8000
## 431 42108.8000 21978.6000 48168.4000 43399.2000 36499.8000
## 432 31612.8000 46134.7000 45069.9000 36348.1000 30723.0000
## 433 21570.2000 18642.1000 21248.8000 21762.0000 16277.0000
## 434 26322.8000 7503.4500 23608.1000 18107.9000 16832.8000
## 435 39628.4000 26108.6000 26247.3000 6323.0000 20248.7000
## 436 24407.5000 24507.0000 82479.9000 74548.8000 49014.0000
## 437 117795.0000 66246.2000 64545.1000 29872.6000 29525.0000
## 438 40027.5000 2294.2700 10428.5000 62552.0000 72999.6000
## 439 99868.5000 69477.0000 79006.2000 145273.0000 100552.0000
## 440 46153.1000 349546.0000 45447.9000 48615.5000 88034.7000
## 441 45233.7000 62989.7000 67720.3000 49696.9000 56691.8000
## 442 60455.3000 102722.0000 75073.3000 90936.8000 57140.9000
## 443 58081.6000 69542.2000 42241.2000 45939.1000 56794.6000
## 444 27147.0000 54751.5000 20064.6000 37429.2000 49344.6000
## 445 125447.0000 112806.0000 76650.1000 51883.8000 41356.9000
## 446 47595.7000 40491.8000 9393.5500 33571.7000 89926.3000
## 447 53795.6000 64669.5000 61300.0000 44520.3000 55095.8000
## 448 48276.1000 34476.4000 21529.2000 24570.1000 45727.5000
## 449 35841.7000 47252.7000 36529.7000 22294.4000 79405.8000
## 450 42938.4000 186871.0000 67663.3000 62810.0000 55623.9000
## 451 105603.0000 38660.4000 38539.7000 57481.0000 54679.8000
## 452 84341.8000 48276.4000 54475.3000 63259.0000 52754.8000
## 453 38855.9000 22341.3000 7392.2100 37450.6000 18014.0000
## 454 52201.8000 55330.1000 42855.3000 101392.0000 38017.1000
## 455 48710.4000 61388.8000 76084.1000 58148.4000 61684.8000
## 456 49682.8000 47728.8000 44875.0000 55386.4000 43429.3000
## 457 100759.0000 13180.9000 17426.6000 65921.0000 313544.0000
## 458 29537.1000 55241.2000 48698.3000 14187.1000 41612.9000
## 459 43952.7000 17292.5000 86239.7000 68452.4000 42712.9000
## 460 44388.8000 44612.3000 101510.0000 57740.7000 48517.4000
## 461 44047.3000 58129.9000 62758.7000 29403.6000 55930.9000
## 462 52879.7000 83999.6000 52476.2000 74796.6000 15255.0000
## 463 85369.9000 60200.2000 43700.8000 55417.3000 84410.4000
## 464 30227.6000 78418.0000 30417.5000 115186.0000 57068.6000
## 465 22842.8000 28386.9000 27313.8000 2919.1300 78270.7000
## 466 74317.7000 59726.9000 62290.2000 76768.7000 86899.1000
## 467 76591.4000 69914.7000 100476.0000 75496.5000 113386.0000
## 468 64385.0000 82429.0000 73146.0000 124088.0000 81210.9000
## 469 65264.3000 66090.7000 60104.3000 68695.8000 90682.9000
## 470 90835.7000 51851.7000 43921.5000 31739.5000 36501.0000
## 471 43603.1000 79139.7000 64759.0000 94738.2000 60954.4000
## 472 44607.9000 81124.8000 56195.2000 37080.1000 73963.9000
## 473 28098.1000 20151.8000 92637.1000 72085.7000 67882.2000
## 474 90102.6000 73380.8000 74852.5000 79202.2000 92004.8000
## 475 90141.4000 94880.6000 86810.3000 66799.3000 73018.9000
## 476 72797.9000 60594.5000 81113.3000 87416.2000 71734.8000
## 477 56048.1000 83817.1000 75548.0000 74669.6000 43297.2000
## 478 71792.3000 88667.7000 90781.8000 93204.8000 77209.2000
## 479 53713.9000 42938.4000 43926.4000 40522.7000 34315.8000
## 480 65290.6000 19499.7000 47962.6000 50779.5000 85866.8000
## 481 90137.9000 64760.2000 29220.1000 43493.4000 73113.9000
## 482 69880.7000 59040.0000 57923.9000 50985.8000 56074.3000
## 483 33949.4000 85761.6000 100238.0000 51176.1000 28389.6000
## 484 66293.7000 54190.8000 69519.3000 47860.4000 55461.0000
## 485 11083.5000 50379.4000 39235.1000 39716.1000 50013.6000
## 486 44272.4000 37957.6000 45626.2000 1038.1700 39291.7000
## 487 11460.2000 32320.6000 31552.4000 41736.5000 40361.8000
## 488 25558.6000 29068.8000 35904.4000 28596.4000 71891.0000
## 489 34927.7000 33025.0000 19756.8000 43635.1000 40602.3000
## 490 2230.6200 35125.1000 45829.0000 27663.5000 48649.4000
## 491 117125.0000 6448.5600 86522.3000 111038.0000 129258.0000
## 492 40930.4000 47039.5000 38456.2000 26784.8000 41098.2000
## 493 63361.8000 37874.6000 47054.7000 21740.4000 21201.7000
## 494 25961.2000 34362.4000 33996.4000 40041.1000 36158.8000
## 495 40143.0000 40739.3000 50637.2000 45825.7000 35203.4000
## 496 21618.8000 41599.0000 32527.8000 1508.0300 41984.2000
## 497 24640.9000 29783.0000 2534.4700 18661.8000 23716.7000
## 498 35782.0000 14706.6000 42945.2000 72293.1000 41766.8000
## 499 46289.2000 32289.3000 44879.4000 38288.3000 41446.6000
## 500 38876.3000 83692.4000 52394.6000 56912.4000 37927.7000
## 501 50795.0000 19856.9000 16619.3000 32701.3000 36151.7000
## 502 6675.6200 16729.5000 18584.3000 56801.1000 28997.6000
## 503 65214.0000 47081.7000 84968.1000 46920.8000 35002.4000
## 504 40916.2000 29461.8000 48566.5000 51389.4000 36961.1000
## 505 19682.6000 31806.5000 24799.0000 8877.5400 28017.6000
## 506 39862.1000 45371.7000 48789.5000 47322.7000 78947.5000
## 507 429232.0000 116082.0000 126737.0000 47651.7000 31412.9000
## 508 48668.0000 95394.3000 94890.2000 89357.1000 64880.9000
## 509 160428.0000 226021.0000 82666.1000 52268.8000 90397.3000
## 510 62980.7000 64830.4000 52680.1000 78447.8000 15208.7000
## 511 31634.2000 8564.5000 56704.1000 66933.6000 109045.0000
## 512 63236.5000 70368.1000 43559.2000 38106.0000 47573.4000
## 513 57607.9000 57915.6000 54179.1000 67835.7000 63117.7000
## 514 139216.0000 67845.7000 71590.9000 63478.0000 43408.5000
## 515 70158.9000 50574.2000 157969.0000 45585.0000 39253.9000
## 516 96728.4000 47867.1000 43334.2000 51010.9000 49161.0000
## 517 26635.5000 36999.0000 23073.6000 31955.9000 33647.9000
## 518 35302.1000 32911.3000 37448.6000 35033.0000 41526.8000
## 519 55092.9000 42918.9000 34365.7000 29403.6000 10020.1000
## 520 41501.6000 42157.7000 33221.7000 50281.9000 25711.6000
## 521 44438.8000 39670.5000 54751.5000 49271.1000 41542.7000
## 522 30670.3000 35060.8000 32445.3000 25992.9000 25054.6000
## 523 27252.4000 24143.0000 19220.0000 29374.1000 31176.5000
## 524 22992.5000 35324.6000 16591.3000 42241.2000 45670.6000
## 525 20128.2000 23610.0000 28012.8000 27106.8000 23963.9000
## 526 39073.1000 65975.7000 76129.4000 68992.9000 56228.8000
## 527 49980.3000 43207.8000 11486.5000 25110.9000 36715.8000
## 528 66478.6000 58238.4000 70531.2000 42327.7000 41088.0000
## 529 80031.4000 57600.8000 59275.1000 58228.2000 35270.8000
## 530 8111.3300 6625.9600 34052.2000 30142.7000 30227.6000
## 531 31963.4000 24810.3000 29918.5000 20470.4000 22802.6000
## 532 39530.7000 39931.1000 32616.0000 28750.6000 26209.4000
## 533 31933.6000 33804.1000 26847.0000 18106.2000 35515.2000
## 534 18076.8000 49728.5000 37583.0000 23209.9000 25304.4000
## 535 15823.4000 13352.3000 15790.5000 17763.0000 7429.8300
## 536 24466.7000 25328.1000 24703.6000 15407.7000 12623.9000
## 537 5027.1900 8113.7500 10795.5000 7906.8900 18351.7000
## 538 18756.9000 16144.3000 14106.2000 15264.8000 18379.6000
## 539 10154.7000 9048.1900 12884.2000 14086.3000 8611.4200
## 540 17520.8000 9103.1500 38084.7000 29814.5000 37028.6000
## 541 13300.2000 31566.8000 37857.9000 51885.2000 16237.1000
## 542 14605.2000 21399.0000 20684.4000 15239.4000 20753.7000
## 543 28512.8000 21411.8000 25372.7000 23008.5000 21613.7000
## 544 16896.5000 31960.9000 47473.2000 20278.3000 41054.3000
## 545 20473.5000 23565.0000 12268.1000 21636.6000 25347.9000
## 546 22755.8000 12342.4000 6806.9300 7209.1400 16474.1000
## 547 14587.3000 13728.4000 22415.5000 13692.3000 52447.8000
## 548 10810.1000 12906.7000 23367.6000 20954.4000 39170.9000
## 549 20154.0000 35496.0000 17866.9000 26726.4000 23635.9000
## 550 27325.9000 27566.0000 13155.4000 13666.5000 18300.3000
## 551 16554.1000 38017.1000 18175.9000 1022.3400 29835.9000
## 552 24152.1000 10325.0000 15909.8000 5330.3300 22435.6000
## 553 23131.1000 48013.7000 37422.9000 29173.8000 42816.9000
## 554 68991.3000 19997.8000 52090.0000 71234.9000 14801.5000
## 555 29616.5000 22732.4000 24093.1000 20579.1000 30435.2000
## 556 18604.6000 30667.2000 10645.5000 19785.4000 6722.2800
## 557 43416.7000 61687.7000 43064.1000 64604.3000 38390.1000
## 558 65707.5000 99004.1000 34808.6000 33991.2000 44983.0000
## 559 35621.2000 57636.2000 60035.2000 73683.6000 39014.4000
## 560 50832.3000 6804.2800 54956.0000 59894.2000 31489.5000
## 561 96552.3000 147844.0000 20927.2000 17671.1000 21440.9000
## 562 29164.6000 20748.7000 50055.2000 30050.3000 28753.3000
## 563 28489.6000 41532.4000 53613.4000 28716.3000 36722.8000
## 564 29121.0000 14198.4000 43442.2000 42565.0000 35905.0000
## 565 30186.4000 19990.6000 31346.2000 38313.1000 23232.7000
## 566 33828.0000 28491.5000 30340.2000 24979.8000 11836.4000
## 567 46879.2000 45626.2000 38288.3000 66452.2000 20278.3000
## 568 35909.1000 35996.4000 44983.0000 66149.7000 55728.6000
## 569 33938.0000 50241.8000 29442.1000 22665.2000 24047.0000
## 570 23519.2000 19596.6000 45113.3000 31020.5000 35413.6000
## 571 817.8740 10727.3000 24671.2000 19011.1000 21877.0000
## 572 24916.1000 58030.6000 28212.5000 28510.4000 25149.3000
## 573 44978.8000 55341.4000 31999.2000 35451.1000 39668.3000
## 574 32059.3000 22211.2000 21625.7000 19844.0000 20000.7000
## 575 37699.0000 43113.2000 35488.7000 30309.2000 42938.6000
## 576 32324.5000 22691.7000 22166.9000 22166.9000 21593.9000
## 577 30367.4000 23058.8000 34632.6000 29892.3000 38711.8000
## 578 54196.5000 48474.0000 65904.5000 57724.1000 51817.5000
## 579 45403.8000 52469.7000 44137.8000 37457.1000 71002.2000
## 580 25002.8000 40351.3000 16168.7000 22236.4000 17340.6000
## 581 11027.6000 19661.0000 14311.2000 29426.8000 28885.5000
## 582 26511.1000 30843.6000 30628.4000 27780.9000 32661.5000
## 583 28676.5000 17118.2000 21508.2000 29688.0000 24514.4000
## 584 70974.1000 62290.2000 39642.9000 28161.2000 35011.9000
## 585 28578.9000 26061.5000 23287.6000 25907.1000 8618.2900
## 586 30085.3000 29293.5000 39372.6000 24162.1000 90460.8000
## 587 34041.8000 44866.2000 36616.0000 34862.2000 30417.5000
## 588 17722.3000 17850.2000 23513.9000 12964.3000 10189.9000
## 589 22688.6000 37053.6000 43311.4000 76655.6000 29750.2000
## 590 48794.0000 28479.7000 19219.7000 55053.8000 42650.7000
## 591 67481.0000 30818.3000 28678.6000 40201.0000 28324.6000
## 592 28705.6000 24521.1000 29090.7000 22858.8000 42938.4000
## 593 35265.6000 23791.7000 21470.2000 26673.9000 24496.3000
## 594 23001.1000 26229.4000 25189.7000 19453.2000 2146.9200
## 595 45626.2000 32976.0000 25305.5000 27867.8000 28936.8000
## 596 15572.5000 31145.1000 30844.9000 10048.9000 24883.1000
## 597 34174.6000 22668.2000 32607.9000 24738.9000 21240.8000
## 598 31204.2000 34635.4000 27326.6000 52831.4000 36246.0000
## 599 25618.9000 23399.0000 21986.2000 15567.3000 20244.5000
## 600 60905.1000 68392.1000 80665.4000 20278.3000 81113.3000
## 601 31855.7000 30453.3000 35668.6000 34210.5000 29729.2000
## 602 13701.8000 43058.3000 48119.8000 33150.3000 29297.6000
## 603 17309.4000 19008.5000 15161.0000 31265.9000 57466.3000
## 604 40355.8000 35575.5000 38412.3000 10075.9000 32854.4000
## 605 48279.5000 60311.9000 24513.0000 41890.8000 40360.4000
## 606 23236.7000 50.6958 32274.2000 39542.7000 33370.5000
## 607 40164.6000 39970.0000 21961.3000 14534.4000 41016.4000
## 608 38881.9000 46495.6000 48726.1000 33975.1000 39387.0000
## 609 34921.1000 28785.6000 26110.3000 30572.3000 40303.5000
## 610 39554.2000 33303.9000 44480.9000 39426.9000 32445.3000
## 611 29750.2000 48405.7000 28730.0000 20292.7000 422.4120
## 612 38338.5000 37003.8000 60455.3000 434970.0000 48694.7000
## 613 37706.2000 41170.9000 52588.4000 13296.5000 70974.1000
## 614 65206.2000 44728.9000 62290.2000 46395.0000 29068.8000
## 615 33370.9000 30567.5000 63543.7000 39073.1000 37423.5000
## 616 29995.0000 46539.6000 26400.8000 35265.6000 80977.3000
## 617 17806.3000 25557.7000 19154.3000 33210.1000 33020.3000
## 618 39530.7000 36717.5000 21801.6000 58354.1000 33078.5000
## 619 19090.6000 49261.9000 20432.2000 33340.2000 21576.0000
## 620 25338.5000 21569.5000 21491.4000 15309.7000 41395.0000
## 621 31024.4000 17422.2000 25580.2000 17581.1000 22577.9000
## 622 20859.0000 32475.2000 30799.5000 28351.2000 24775.2000
## 623 24209.0000 24182.1000 26400.8000 23393.6000 401780.0000
## 624 37939.6000 45412.4000 38050.7000 32607.0000 20151.8000
## 625 44226.0000 55765.4000 34914.3000 33655.1000 42938.4000
## 626 28477.2000 28108.5000 13570.1000 26517.3000 26366.9000
## 627 21323.3000 13101.2000 42135.3000 14222.4000 20399.0000
## 628 19981.8000 25954.2000 24955.4000 17749.0000 15948.5000
## 629 17519.9000 20487.6000 24745.3000 15852.1000 37374.1000
## 630 22599.0000 25937.0000 18136.6000 9684.3700 20796.1000
## 631 37514.9000 56228.8000 31718.6000 37514.9000 20503.5000
## 632 30346.7000 25954.2000 27173.8000 13298.5000 17221.2000
## 633 33587.1000 34464.6000 18535.4000 47106.2000 44210.3000
## 634 25889.8000 13903.9000 43003.7000 42623.7000 42957.3000
## 635 20617.9000 19396.8000 23657.6000 8305.3600 29839.9000
## 636 42189.5000 27799.7000 28899.6000 34947.3000 24536.2000
## 637 62303.3000 34123.5000 27177.7000 25954.2000 34166.6000
## 638 18989.4000 14836.3000 38154.2000 35214.7000 36183.8000
## 639 60455.3000 38907.1000 44510.9000 23388.1000 17917.1000
## 640 32759.6000 20140.5000 105603.0000 19966.3000 19365.9000
## 641 30081.6000 23334.0000 23922.3000 48582.7000 46372.2000
## 642 92000.7000 71438.3000 77259.2000 79194.3000 63516.2000
## 643 70464.9000 41928.9000 33571.8000 34482.6000 62709.3000
## 644 29560.8000 35257.5000 35538.6000 34131.1000 23015.9000
## 645 16610.7000 31196.2000 29121.0000 27961.4000 18258.4000
## 646 60690.1000 72671.9000 26288.2000 22902.5000 3873.1000
## 647 18973.6000 91252.4000 55969.6000 16442.5000 27647.8000
## 648 13649.2000 20508.0000 67934.9000 49617.9000 20648.5000
## 649 17968.9000 29904.9000 38793.1000 30227.6000 35265.6000
## 650 5174.5500 13789.3000 29269.9000 32712.4000 45852.5000
## 651 19874.6000 10381.7000 26621.6000 16746.1000 21882.4000
## 652 22885.6000 9525.8400 30078.3000 6101.0900 18136.6000
## 653 44343.6000 30916.9000 40129.1000 25443.8000 27563.4000
## 654 37383.5000 28529.5000 24816.7000 85214.0000 74940.6000
## 655 112964.0000 61131.8000 64258.2000 31282.4000 32174.1000
## 656 43269.8000 2323.2700 10560.3000 63744.0000 73922.1000
## Jun Jul Aug Sep Oct
## 1 73957.4000 51895.3000 49198.9000 32865.6000 29250.2000
## 2 6050.5500 62279.3000 53659.0000 164120.0000 64820.6000
## 3 42973.7000 56667.7000 32677.9000 26796.5000 29192.1000
## 4 76589.5000 79728.7000 95088.9000 60046.8000 35619.8000
## 5 19365.3000 55879.6000 40904.3000 21903.9000 85415.6000
## 6 45883.9000 55566.8000 42411.2000 56512.3000 48361.1000
## 7 56078.7000 32346.4000 27054.9000 26407.6000 42188.7000
## 8 32155.8000 46521.0000 6553.8400 49787.4000 15140.3000
## 9 20014.2000 18464.3000 70270.6000 56137.9000 89133.7000
## 10 104414.0000 90910.8000 74983.6000 36106.0000 33743.1000
## 11 34849.6000 107549.0000 38531.9000 37840.8000 73606.3000
## 12 47786.6000 91260.0000 65354.1000 58952.4000 18360.8000
## 13 13230.0000 64195.5000 36498.3000 47286.9000 31941.0000
## 14 30673.4000 43688.1000 25406.3000 52518.5000 28673.4000
## 15 41869.6000 41439.9000 28561.5000 31619.9000 32678.4000
## 16 10798.1000 17726.2000 20205.5000 25169.6000 19753.0000
## 17 27618.8000 28660.9000 22885.3000 31737.8000 33334.3000
## 18 22916.4000 12862.3000 15006.1000 31970.7000 39582.9000
## 19 30564.2000 36748.8000 31204.6000 50471.4000 2048.0700
## 20 33352.9000 31534.9000 43580.2000 24727.5000 34605.0000
## 21 22346.4000 17761.7000 44039.6000 34497.9000 42435.0000
## 22 63754.9000 45147.4000 69790.8000 39622.5000 38756.2000
## 23 15208.5000 17779.4000 27353.4000 27450.4000 28449.1000
## 24 13535.9000 37889.4000 30063.6000 20933.3000 27340.8000
## 25 25401.5000 21300.0000 6127.1600 30964.0000 27817.2000
## 26 19199.2000 49198.4000 32538.2000 24110.6000 23751.7000
## 27 9499.5100 28131.5000 25315.3000 42353.9000 32336.8000
## 28 32436.9000 96495.2000 93748.9000 202415.0000 98307.6000
## 29 68532.3000 55165.2000 52598.9000 52021.1000 133997.0000
## 30 13353.5000 17694.0000 3875.5900 17506.2000 8950.3100
## 31 86123.8000 68800.6000 55736.8000 10718.6000 73078.4000
## 32 89749.8000 43934.6000 46895.7000 19110.1000 20375.1000
## 33 39038.7000 34117.8000 36737.0000 37111.1000 44772.6000
## 34 33548.9000 27067.7000 33799.6000 29046.1000 79874.9000
## 35 10858.7000 71908.6000 64758.2000 35416.2000 32824.1000
## 36 46839.5000 38294.8000 40974.0000 64047.6000 52504.2000
## 37 43765.6000 95449.8000 79147.7000 116129.0000 10084.2000
## 38 52225.9000 74609.4000 82190.3000 41577.2000 82060.2000
## 39 86263.5000 84240.5000 37260.7000 34209.1000 28059.3000
## 40 59964.9000 51015.2000 41730.3000 30326.7000 51951.7000
## 41 85716.1000 52792.9000 73929.0000 40049.3000 38313.7000
## 42 45320.9000 55255.4000 43880.2000 56501.5000 35606.4000
## 43 15402.1000 29817.6000 30691.5000 82533.3000 21689.1000
## 44 49903.7000 43264.1000 35220.4000 65648.2000 41577.2000
## 45 20845.2000 16068.3000 18211.3000 16859.2000 28573.9000
## 46 43565.2000 49215.4000 38133.7000 34606.5000 29649.7000
## 47 20718.1000 40760.1000 29151.0000 8531.6500 8736.6700
## 48 34002.8000 26558.1000 36989.1000 29952.3000 26968.9000
## 49 62771.5000 70533.2000 53148.2000 47626.7000 38084.6000
## 50 11067.7000 8590.1700 11092.7000 5999.3500 17042.4000
## 51 28867.2000 24314.0000 21868.1000 21036.8000 27099.0000
## 52 8192.3000 22786.7000 17518.3000 50103.6000 14804.0000
## 53 54174.6000 85617.6000 60902.5000 46688.6000 28512.3000
## 54 46391.7000 20833.1000 42694.8000 43280.1000 44114.6000
## 55 46485.3000 34108.3000 44626.8000 48982.4000 31743.9000
## 56 48036.9000 43378.1000 93001.6000 48676.0000 46038.7000
## 57 32898.6000 35350.1000 35300.1000 12499.8000 7503.0300
## 58 33480.0000 30252.5000 37274.9000 37320.9000 7291.5800
## 59 16473.4000 17543.6000 42512.9000 33032.2000 48668.0000
## 60 39083.3000 60024.2000 33463.4000 28275.3000 25152.1000
## 61 37909.4000 30635.8000 44572.9000 55736.8000 92336.1000
## 62 31715.3000 31530.4000 65838.0000 58053.3000 52013.6000
## 63 89997.2000 85162.7000 81502.2000 65119.2000 76889.1000
## 64 45025.3000 28673.0000 24002.4000 28782.7000 8753.0900
## 65 37269.8000 32764.8000 26483.5000 31071.1000 56895.1000
## 66 27633.5000 42004.5000 41209.0000 38587.5000 36598.8000
## 67 42509.4000 32595.0000 48484.6000 52929.5000 47661.7000
## 68 46409.2000 46238.0000 31672.9000 38893.0000 25210.6000
## 69 39697.0000 42202.3000 46120.9000 30507.0000 24895.9000
## 70 63829.2000 31973.4000 59479.7000 52710.7000 37671.1000
## 71 5208.2700 30932.6000 25816.6000 28447.5000 31903.5000
## 72 84445.1000 31890.7000 22817.1000 11147.4000 10718.6000
## 73 25183.5000 81099.5000 56939.4000 83492.5000 22025.3000
## 74 120355.0000 80635.6000 68223.6000 63680.5000 67800.9000
## 75 60275.9000 66318.8000 67329.7000 54166.0000 110170.0000
## 76 12316.0000 39960.5000 39761.0000 50388.6000 58046.2000
## 77 89524.9000 104821.0000 104452.0000 79752.2000 29931.7000
## 78 27536.6000 7054.1600 36118.2000 49800.5000 36743.9000
## 79 24464.0000 14461.8000 34523.6000 28740.3000 26173.0000
## 80 73357.9000 47684.3000 27623.4000 26215.9000 68280.0000
## 81 56845.6000 36993.1000 33858.5000 42919.0000 29858.7000
## 82 100356.0000 86026.2000 63189.6000 57865.1000 45971.0000
## 83 43662.5000 20681.4000 36278.5000 99512.3000 48391.3000
## 84 39448.1000 57346.1000 60485.4000 29345.2000 12288.4000
## 85 18879.1000 51071.5000 49153.8000 23377.1000 26310.6000
## 86 15778.1000 11755.6000 16400.2000 22294.7000 14166.8000
## 87 16251.6000 10182.7000 19101.2000 18559.8000 17022.2000
## 88 47956.1000 27909.2000 8911.5800 15215.2000 25210.6000
## 89 30631.2000 23000.2000 11561.4000 38541.2000 26310.1000
## 90 39539.7000 29551.1000 40483.0000 57565.0000 33105.2000
## 91 70002.6000 65136.5000 50802.1000 27439.6000 30667.2000
## 92 8094.1800 21583.5000 19400.8000 20172.5000 17023.9000
## 93 19844.0000 20103.1000 24884.3000 18693.2000 24071.0000
## 94 40809.2000 24868.3000 28065.9000 30003.3000 29064.4000
## 95 37508.4000 36848.0000 47047.8000 33719.2000 14999.8000
## 96 20727.8000 35544.5000 27353.4000 11647.3000 17383.8000
## 97 23620.6000 31859.2000 19164.9000 30612.5000 28710.6000
## 98 25973.3000 26215.9000 9194.3800 12697.9000 14939.4000
## 99 29541.7000 18474.4000 15594.2000 37078.9000 24434.0000
## 100 104251.0000 74774.7000 68650.9000 38992.4000 55129.1000
## 101 44695.9000 40699.3000 33135.9000 43197.1000 56957.5000
## 102 64582.5000 57291.0000 61708.1000 41666.2000 59302.5000
## 103 22292.7000 34634.7000 36615.5000 71118.8000 40438.6000
## 104 75025.8000 41719.8000 27553.9000 31790.2000 28454.6000
## 105 21733.9000 25407.6000 36103.8000 30434.4000 21437.2000
## 106 66900.9000 47322.5000 43417.6000 59275.9000 51393.6000
## 107 27830.6000 21607.1000 21352.4000 19382.9000 21388.5000
## 108 34292.9000 31573.8000 72915.8000 51127.0000 53593.1000
## 109 25005.6000 20144.2000 18190.4000 21803.7000 18217.8000
## 110 109414.0000 80199.7000 89788.8000 129546.0000 41456.1000
## 111 15849.2000 17377.0000 17687.4000 20059.3000 17799.3000
## 112 34413.7000 35256.8000 44289.6000 30771.9000 38211.4000
## 113 41882.0000 41605.3000 61258.2000 32903.3000 41114.6000
## 114 88741.4000 64805.4000 35609.2000 53640.4000 42681.9000
## 115 43938.5000 76802.8000 27127.7000 30825.6000 36136.0000
## 116 42206.9000 39344.2000 41882.2000 35219.5000 26812.8000
## 117 20686.4000 15492.8000 15190.3000 25582.6000 22923.7000
## 118 78124.0000 49085.6000 65529.8000 30282.5000 32269.9000
## 119 344787.0000 44986.2000 68612.2000 87907.8000 126505.0000
## 120 35246.8000 85990.2000 66493.8000 81445.3000 78569.9000
## 121 27255.7000 24276.5000 92657.7000 71682.6000 101827.0000
## 122 57964.5000 92108.5000 90910.9000 59168.4000 74221.5000
## 123 33462.2000 31007.1000 31084.0000 56751.4000 39723.7000
## 124 35867.5000 48887.3000 26576.2000 24777.6000 33355.5000
## 125 19977.8000 914.9260 47593.1000 33142.0000 36106.4000
## 126 27839.1000 42043.6000 28878.9000 15634.3000 18241.1000
## 127 60268.5000 66534.5000 47596.4000 39799.4000 20638.6000
## 128 35987.1000 33252.1000 26796.5000 17896.7000 19446.3000
## 129 25600.9000 15624.8000 26705.7000 32701.3000 19357.3000
## 130 14876.7000 37259.3000 14206.1000 24054.0000 88750.3000
## 131 26827.0000 10588.5000 16555.6000 13541.5000 13476.2000
## 132 11790.5000 14637.9000 819.2300 17693.7000 8036.4000
## 133 87530.9000 40121.2000 17322.4000 39045.0000 31910.8000
## 134 10941.4000 62105.3000 28772.6000 43215.9000 38591.5000
## 135 47821.9000 70905.6000 85857.4000 70232.8000 54024.9000
## 136 41687.0000 75451.3000 36528.7000 22926.2000 36155.8000
## 137 36019.6000 32821.3000 34139.8000 15624.8000 33186.3000
## 138 22686.9000 21544.9000 17092.9000 47567.6000 32112.4000
## 139 1041.6500 25987.0000 361065.0000 21334.0000 21716.6000
## 140 14583.2000 32139.4000 41181.0000 21951.3000 27353.4000
## 141 27851.2000 9409.5700 28235.9000 38835.2000 26002.2000
## 142 13421.2000 70126.9000 28778.3000 12458.2000 26796.5000
## 143 20281.7000 14619.6000 28828.5000 36490.4000 5120.1900
## 144 63055.8000 55688.4000 53057.1000 59361.4000 55901.5000
## 145 45057.6000 59214.1000 33017.6000 57291.0000 19799.0000
## 146 105174.0000 135441.0000 92251.6000 83642.7000 98307.6000
## 147 26153.9000 21938.3000 26751.1000 22813.3000 59632.1000
## 148 60609.5000 18221.6000 31463.0000 24845.5000 24095.0000
## 149 69406.8000 56560.8000 93001.6000 55750.1000 44203.3000
## 150 102343.0000 25724.7000 67121.0000 75030.3000 57891.3000
## 151 46354.5000 31543.6000 37683.1000 16128.4000 32155.8000
## 152 71993.6000 48633.3000 65648.2000 61971.1000 36228.9000
## 153 64163.6000 55210.2000 58967.2000 57008.6000 45178.2000
## 154 29060.8000 44905.2000 22174.1000 14270.1000 29246.2000
## 155 27835.6000 21261.2000 98825.6000 19633.6000 15557.1000
## 156 26456.3000 29266.5000 13601.4000 30721.1000 35200.8000
## 157 27316.2000 34443.6000 36567.1000 25115.6000 24885.0000
## 158 39167.8000 31387.6000 36875.8000 37201.5000 18527.3000
## 159 10624.9000 32463.3000 32298.2000 29809.2000 24028.7000
## 160 41663.3000 18477.3000 12708.4000 17291.5000 7680.2800
## 161 27339.4000 24697.2000 2178.9500 22039.8000 14664.4000
## 162 35982.9000 25329.6000 28124.7000 21066.1000 28441.7000
## 163 93695.6000 88482.4000 21866.8000 42548.9000 25109.8000
## 164 13184.3000 22537.9000 28379.9000 28459.7000 21617.3000
## 165 16802.4000 35012.4000 37064.4000 35764.1000 48937.0000
## 166 66745.3000 71110.7000 50378.6000 98472.2000 74674.3000
## 167 157833.0000 196762.0000 35318.4000 36935.2000 39825.4000
## 168 14175.7000 20450.8000 77033.7000 54664.9000 6249.9200
## 169 49175.5000 56919.5000 53742.7000 49451.9000 59083.3000
## 170 50433.1000 58638.9000 51470.1000 44730.1000 37515.1000
## 171 33219.9000 64917.5000 79085.0000 78321.6000 71682.6000
## 172 24501.5000 30635.8000 21273.1000 20894.8000 34567.0000
## 173 18221.6000 21759.3000 13069.2000 19998.2000 24994.8000
## 174 28540.4000 19262.1000 56434.5000 42809.2000 71385.8000
## 175 46326.6000 19293.5000 65547.6000 60472.9000 41538.8000
## 176 50629.8000 31818.2000 33775.1000 32251.8000 44600.3000
## 177 51990.3000 51069.6000 24999.7000 57538.9000 76539.2000
## 178 35434.2000 24196.7000 52403.9000 12243.9000 39582.9000
## 179 74763.8000 70374.3000 176857.0000 194698.0000 59843.3000
## 180 66562.4000 40045.6000 40337.0000 25492.5000 43902.6000
## 181 82060.2000 48736.3000 43967.3000 40104.6000 61360.6000
## 182 20270.8000 24713.6000 22445.6000 3276.9200 29388.3000
## 183 27060.9000 24920.2000 6413.1200 20460.8000 21962.7000
## 184 59690.7000 28573.1000 30147.0000 21026.0000 18947.3000
## 185 15386.6000 8627.9200 20104.9000 2606.8500 55857.8000
## 186 19181.2000 36386.4000 21437.2000 19892.6000 22768.0000
## 187 26086.8000 19507.9000 26558.1000 26709.4000 14748.7000
## 188 30307.6000 39700.9000 107384.0000 77124.8000 212932.0000
## 189 53723.9000 70745.2000 47029.4000 389134.0000 27353.4000
## 190 32155.8000 45341.3000 43963.2000 58551.4000 34299.6000
## 191 31632.7000 29547.2000 27290.7000 34006.6000 84482.3000
## 192 30912.5000 16297.3000 14336.5000 24813.1000 38601.4000
## 193 25250.7000 18235.7000 29165.7000 9821.4500 20910.7000
## 194 71792.0000 38060.0000 120355.0000 6645.5400 78124.0000
## 195 21040.2000 22188.3000 30603.5000 16412.0000 16077.9000
## 196 43062.3000 104165.0000 30176.6000 43831.3000 48848.6000
## 197 39562.4000 40595.5000 25165.1000 53385.4000 40483.0000
## 198 76403.1000 94359.8000 41625.2000 42055.4000 39388.9000
## 199 29244.3000 16077.9000 46782.3000 45464.8000 45148.8000
## 200 25906.4000 20221.8000 21873.5000 11296.4000 25251.9000
## 201 15751.6000 25210.6000 21437.2000 31637.8000 27422.5000
## 202 35449.8000 35102.7000 37396.7000 29333.7000 43228.6000
## 203 25461.3000 17522.6000 10941.4000 23057.5000 20243.1000
## 204 66383.8000 30959.3000 39800.7000 18723.3000 19203.8000
## 205 27138.4000 18012.1000 22316.4000 28374.4000 17778.1000
## 206 43757.7000 36550.4000 37499.5000 51697.4000 35715.6000
## 207 26133.9000 9173.5100 50360.5000 30065.8000 27349.8000
## 208 10542.5000 16077.9000 17866.5000 26470.1000 18772.2000
## 209 52648.0000 43624.0000 22117.0000 13522.4000 5042.1200
## 210 59741.5000 50543.3000 55595.8000 46476.9000 18228.9000
## 211 12807.0000 11092.7000 35389.6000 64020.5000 38549.8000
## 212 41977.5000 53736.9000 45826.8000 25210.6000 63788.0000
## 213 34028.6000 28075.6000 35351.4000 25055.6000 21827.0000
## 214 11799.5000 5931.7200 16249.8000 9529.3000 3129.5000
## 215 17306.2000 15936.5000 14106.1000 11246.7000 18299.1000
## 216 43679.7000 54462.6000 43070.4000 39079.5000 45180.3000
## 217 30252.8000 25169.0000 17588.1000 35615.5000 32499.8000
## 218 24946.7000 7291.5800 172570.0000 36095.1000 20913.9000
## 219 23451.0000 29023.4000 25999.1000 46371.6000 59260.8000
## 220 51260.8000 78501.9000 97342.7000 67638.6000 59555.8000
## 221 83888.8000 78877.0000 45930.4000 176622.0000 45526.3000
## 222 82372.0000 42303.3000 62407.5000 65316.3000 53455.4000
## 223 10095.8000 96968.5000 100656.0000 69749.6000 87542.1000
## 224 64656.8000 57356.9000 59799.1000 41714.1000 56315.2000
## 225 50999.7000 46117.2000 26875.4000 54068.3000 19814.2000
## 226 77236.3000 61154.3000 127164.0000 98708.7000 75226.4000
## 227 45625.9000 57161.2000 40336.3000 41217.2000 9623.2600
## 228 68176.4000 64696.3000 55928.4000 57717.0000 66845.2000
## 229 72683.4000 84589.8000 46351.0000 36517.5000 19192.8000
## 230 55800.9000 99445.9000 60725.6000 35647.0000 53212.7000
## 231 77639.0000 89019.2000 56327.9000 184539.0000 67562.8000
## 232 41505.8000 104285.0000 52595.7000 40465.1000 54248.6000
## 233 39240.0000 86052.5000 71894.0000 55410.9000 63273.1000
## 234 40436.1000 24246.7000 7299.9600 172768.0000 37911.6000
## 235 37411.0000 51546.9000 54131.8000 42315.6000 100126.0000
## 236 47705.9000 49603.2000 54711.2000 57182.8000 59618.7000
## 237 56070.8000 44537.0000 45428.4000 47279.2000 53894.7000
## 238 51375.3000 157745.0000 13016.4000 43653.7000 68896.9000
## 239 41033.0000 30854.4000 53295.1000 48099.1000 13854.8000
## 240 55073.1000 43302.3000 19078.1000 83701.3000 62534.8000
## 241 40258.7000 58509.0000 41774.0000 44055.6000 103335.0000
## 242 74727.2000 50720.5000 48143.2000 56484.0000 62940.5000
## 243 69871.1000 59798.6000 70912.5000 52095.1000 79446.3000
## 244 20857.0000 82969.6000 46606.6000 51357.7000 58866.5000
## 245 65207.9000 48958.5000 49775.8000 30037.9000 47726.9000
## 246 40575.3000 20712.1000 20216.3000 22506.2000 7517.5500
## 247 43816.0000 75326.8000 61066.1000 61512.9000 75944.8000
## 248 77119.1000 43996.9000 24343.9000 75717.2000 67396.5000
## 249 38944.2000 20025.3000 35298.3000 78872.1000 82365.4000
## 250 47408.3000 86266.4000 61892.0000 57961.7000 62805.1000
## 251 64656.8000 95926.1000 86264.9000 88666.8000 50995.0000
## 252 59392.2000 49222.3000 35803.8000 43059.0000 76704.4000
## 253 52111.9000 68817.2000 44469.7000 72429.8000 65790.4000
## 254 67030.0000 51357.0000 49647.0000 27789.7000 93336.8000
## 255 53042.3000 90451.2000 86324.0000 75435.4000 55800.9000
## 256 80101.1000 71538.2000 32321.4000 133726.0000 87910.7000
## 257 63990.9000 33532.7000 78441.2000 74619.8000 69992.3000
## 258 105496.0000 30756.4000 97645.2000 55780.7000 88302.2000
## 259 145999.0000 107309.0000 87753.6000 62842.1000 50479.2000
## 260 44902.3000 46756.3000 52775.8000 50645.6000 42402.6000
## 261 46215.1000 21605.1000 21605.1000 63707.3000 18916.8000
## 262 70522.5000 87110.0000 76879.4000 47999.9000 71232.4000
## 263 46501.2000 91252.7000 67428.7000 55256.1000 49486.9000
## 264 54339.3000 56650.6000 77123.9000 100991.0000 49732.4000
## 265 72966.5000 72224.0000 41295.7000 68651.8000 58797.5000
## 266 38894.8000 28613.0000 47385.7000 14257.1000 44512.2000
## 267 48289.2000 45056.9000 1025.2100 19792.0000 26370.4000
## 268 28101.7000 35830.5000 41898.7000 38779.2000 52845.2000
## 269 33288.6000 27751.2000 196841.0000 51097.5000 27287.1000
## 270 41765.4000 37100.2000 51260.8000 33967.5000 42922.9000
## 271 44998.6000 20800.7000 15467.5000 11414.4000 53468.3000
## 272 143107.0000 48110.6000 41161.7000 19827.5000 119561.0000
## 273 41351.6000 65817.3000 30738.0000 54163.8000 46309.6000
## 274 21512.1000 31068.5000 16393.6000 21602.1000 18697.6000
## 275 36215.7000 38494.6000 44805.6000 43780.7000 58524.9000
## 276 40903.4000 38504.7000 33117.2000 32834.3000 29208.2000
## 277 48289.2000 1436.6800 42087.7000 36700.1000 37542.7000
## 278 4830.1600 17964.2000 22870.0000 26603.4000 16740.3000
## 279 14425.2000 32192.8000 43080.6000 66882.7000 46372.5000
## 280 32712.6000 30365.2000 33398.4000 49210.3000 23760.7000
## 281 70139.5000 11277.4000 21780.7000 32486.8000 28787.4000
## 282 5126.0800 23888.8000 26857.4000 15639.1000 30087.9000
## 283 5580.0900 32192.8000 74967.5000 61038.2000 25095.4000
## 284 85063.1000 25239.6000 28405.3000 44058.6000 31308.0000
## 285 46798.6000 56243.7000 36499.8000 38925.6000 53097.2000
## 286 26157.4000 9043.0900 18127.6000 26422.6000 39538.4000
## 287 59741.6000 55442.3000 80154.3000 32282.8000 63614.0000
## 288 45771.0000 30787.0000 53300.9000 35188.6000 29320.5000
## 289 88134.2000 50063.2000 73758.0000 85263.5000 206409.0000
## 290 46517.1000 76896.0000 87981.3000 105020.0000 78370.0000
## 291 54390.4000 69379.5000 15019.0000 65559.7000 79333.6000
## 292 65322.5000 38757.6000 51488.8000 51220.2000 60575.1000
## 293 42139.7000 43182.1000 50233.4000 39267.7000 37357.9000
## 294 68963.9000 65007.3000 56136.6000 66665.7000 54320.0000
## 295 25031.6000 41039.5000 6257.1100 92084.0000 91271.4000
## 296 49031.1000 52796.2000 39271.1000 30410.2000 42972.5000
## 297 47952.8000 45718.1000 37223.2000 45394.4000 44937.2000
## 298 26592.7000 39279.0000 23193.8000 49469.7000 29482.1000
## 299 31086.6000 38426.7000 32720.5000 32592.3000 31119.7000
## 300 66922.5000 50772.0000 39162.6000 53052.9000 30909.1000
## 301 23545.0000 23596.2000 28073.1000 26363.9000 20351.2000
## 302 31234.0000 34311.7000 25963.5000 24789.5000 26782.6000
## 303 24255.0000 32294.5000 37558.3000 26719.3000 22882.5000
## 304 26249.5000 26765.5000 26798.0000 26651.5000 48289.2000
## 305 16667.2000 12877.1000 21278.2000 30444.7000 32192.8000
## 306 24010.1000 27539.9000 21818.8000 22034.3000 50972.3000
## 307 55527.2000 100471.0000 66529.2000 76274.8000 86242.9000
## 308 86482.0000 65591.8000 56245.8000 91742.0000 59235.5000
## 309 34274.5000 34339.0000 36805.5000 35653.1000 67464.6000
## 310 23220.5000 32192.8000 60589.5000 63592.8000 48679.5000
## 311 56446.2000 44734.8000 40048.2000 46746.9000 79796.3000
## 312 24646.4000 11295.6000 7240.7600 11105.4000 9226.9300
## 313 15143.2000 19361.8000 27258.8000 2885.6500 9086.2600
## 314 36814.2000 27225.5000 28147.5000 29704.6000 20932.4000
## 315 17195.7000 11058.1000 22310.2000 13354.5000 18111.3000
## 316 14759.8000 13432.5000 24829.5000 24716.1000 25754.3000
## 317 9079.5200 22065.7000 8692.0600 10999.6000 12582.5000
## 318 15744.0000 14208.9000 13060.9000 18559.8000 12115.0000
## 319 16084.5000 11667.6000 12850.5000 1312.4600 14762.5000
## 320 14286.2000 9248.0300 11976.3000 16750.2000 10502.2000
## 321 29964.4000 29449.7000 32328.4000 26287.5000 33688.6000
## 322 17567.7000 39817.8000 25630.4000 20522.1000 18304.6000
## 323 18696.4000 16394.3000 18450.9000 19554.7000 35814.1000
## 324 19720.3000 19320.4000 19281.3000 19819.3000 22114.0000
## 325 27626.3000 39704.5000 50077.9000 23437.8000 43703.3000
## 326 4292.3800 25031.6000 15813.9000 18803.2000 38590.2000
## 327 7338.7500 16268.5000 13065.0000 2624.9000 15902.1000
## 328 49710.6000 30961.5000 25620.0000 11584.6000 12115.0000
## 329 30721.3000 22996.4000 4505.6900 19664.7000 18239.0000
## 330 23431.9000 21409.4000 45069.9000 15470.3000 21714.2000
## 331 13295.7000 17689.2000 3923.5300 9530.5900 10568.6000
## 332 34143.3000 14141.4000 25580.5000 20863.3000 5531.7600
## 333 20330.3000 3656.8400 18433.1000 15622.2000 22825.8000
## 334 30244.6000 39860.9000 26902.8000 38050.2000 37770.3000
## 335 73830.0000 14374.4000 12975.6000 10657.2000 9719.5000
## 336 23755.5000 29488.1000 35251.1000 22383.3000 32306.7000
## 337 17109.0000 22036.5000 7139.2200 21682.0000 18546.4000
## 338 62197.7000 42315.9000 60254.1000 43123.1000 47333.1000
## 339 95791.8000 35093.1000 35099.6000 42402.6000 34522.1000
## 340 68038.3000 58082.6000 71307.3000 44195.6000 164948.0000
## 341 58215.6000 3921.2800 68104.1000 56926.4000 32279.2000
## 342 97275.7000 111057.0000 22399.7000 17346.1000 24705.7000
## 343 29159.7000 17827.8000 48220.4000 29384.6000 32781.1000
## 344 30287.6000 53576.6000 37557.9000 37228.1000 25346.1000
## 345 29658.7000 26706.3000 48587.7000 42033.8000 35457.0000
## 346 24001.3000 30509.6000 37095.0000 22942.7000 12877.1000
## 347 42367.7000 34587.5000 40777.6000 28339.0000 30010.6000
## 348 21567.7000 44529.4000 64418.3000 47648.4000 45056.9000
## 349 21690.9000 36499.8000 22345.0000 25294.3000 53185.1000
## 350 43059.0000 36565.3000 31035.9000 27246.1000 20022.8000
## 351 24301.3000 22718.6000 23309.1000 28090.7000 29271.2000
## 352 25555.1000 26495.3000 78034.1000 18108.3000 21807.7000
## 353 17599.1000 14483.5000 27241.8000 27693.4000 24605.2000
## 354 18171.5000 10628.0000 41349.8000 45694.9000 49411.4000
## 355 23998.6000 25471.9000 33966.7000 2002.5300 40144.5000
## 356 19810.7000 41126.6000 28890.7000 27900.4000 44344.8000
## 357 28496.0000 24884.8000 23803.2000 21940.2000 52116.9000
## 358 31198.9000 24226.5000 33457.7000 31233.2000 38410.9000
## 359 54029.7000 49196.8000 65082.2000 56198.6000 57335.2000
## 360 58493.1000 52026.8000 44060.2000 37558.3000 41046.8000
## 361 27403.1000 25466.3000 32532.5000 15535.3000 26483.2000
## 362 18575.5000 15066.5000 19539.8000 16470.4000 27625.1000
## 363 31413.3000 31085.0000 27323.0000 27372.8000 31480.9000
## 364 42842.0000 26603.5000 30376.3000 26107.5000 19477.8000
## 365 26033.1000 36434.8000 28788.9000 45775.2000 61512.9000
## 366 17140.5000 24760.4000 27343.1000 27322.3000 30884.1000
## 367 19560.3000 16096.4000 30741.8000 11512.6000 15133.2000
## 368 30399.1000 47280.5000 15642.8000 31977.9000 29641.4000
## 369 42005.0000 23222.1000 85513.9000 30037.9000 16482.3000
## 370 16192.5000 25553.8000 15961.2000 20142.7000 38446.3000
## 371 25239.6000 35036.3000 40221.3000 48185.1000 29040.5000
## 372 41818.9000 42906.6000 51283.4000 66639.0000 29998.5000
## 373 30330.4000 18261.7000 49255.0000 34086.5000 27813.9000
## 374 23606.8000 18699.0000 31191.8000 28660.3000 82628.2000
## 375 15447.7000 25915.6000 74108.5000 36238.1000 23373.3000
## 376 38234.0000 40715.8000 45056.9000 33368.9000 26312.1000
## 377 26490.9000 27194.4000 15378.2000 30756.4000 36011.5000
## 378 31083.8000 57412.0000 33768.6000 26251.1000 26076.9000
## 379 22787.0000 21367.0000 34750.9000 30275.1000 33879.5000
## 380 21461.9000 29335.3000 34609.2000 64385.6000 23721.4000
## 381 40248.6000 61077.1000 46010.9000 43592.0000 55832.5000
## 382 10730.9000 29007.1000 23522.3000 25654.2000 29859.5000
## 383 45120.7000 55800.9000 37542.1000 42571.3000 20999.4000
## 384 39719.6000 161534.0000 47157.0000 85814.7000 200253.0000
## 385 43096.4000 120152.0000 43987.5000 44522.7000 49936.0000
## 386 30265.2000 47875.4000 42873.0000 61926.0000 66639.0000
## 387 34470.6000 34952.6000 33565.3000 22946.3000 20793.4000
## 388 45353.1000 54125.3000 25396.6000 38740.8000 37368.3000
## 389 68086.0000 37558.3000 47189.5000 43475.5000 46048.0000
## 390 28999.6000 43230.3000 34373.0000 28518.9000 34486.6000
## 391 40117.7000 35852.3000 46217.3000 16020.2000 34230.6000
## 392 31049.0000 28970.7000 37542.7000 4005.0600 29378.9000
## 393 24682.5000 35257.4000 47050.0000 39599.3000 37673.1000
## 394 40902.4000 21461.9000 23698.7000 25956.4000 35055.1000
## 395 39191.6000 32119.8000 42851.7000 56641.9000 61372.4000
## 396 62325.4000 42606.6000 42174.2000 48395.5000 38069.5000
## 397 30147.3000 74359.6000 31343.0000 30794.8000 28237.6000
## 398 35457.0000 42181.3000 47885.7000 16278.9000 27710.8000
## 399 25096.7000 57668.9000 42291.6000 60356.4000 21529.5000
## 400 23644.5000 29373.8000 24585.4000 30276.0000 16099.4000
## 401 15642.8000 27276.5000 32451.9000 21165.9000 27189.0000
## 402 27927.6000 23816.0000 13694.3000 37933.0000 30915.1000
## 403 22890.4000 24979.8000 19096.8000 12210.9000 29949.9000
## 404 47760.6000 51275.3000 52286.0000 24083.9000 28973.5000
## 405 48220.0000 38542.6000 41623.9000 36804.8000 29098.9000
## 406 30263.0000 10252.2000 43045.5000 48160.6000 43674.2000
## 407 19404.9000 29683.7000 29100.6000 27969.1000 27571.0000
## 408 31219.2000 18998.1000 21328.4000 10252.2000 34474.1000
## 409 11466.9000 18924.6000 17905.4000 25630.4000 18242.6000
## 410 14659.4000 18662.6000 32323.2000 16096.4000 24988.8000
## 411 35535.0000 22317.0000 24242.1000 5365.4700 9388.0500
## 412 37046.8000 55527.2000 42043.2000 37046.8000 21469.3000
## 413 30855.3000 25630.4000 25838.2000 27583.0000 28268.4000
## 414 26794.7000 23228.9000 22502.4000 46630.2000 43540.6000
## 415 13730.4000 42278.0000 44677.4000 42154.7000 17941.3000
## 416 21577.2000 19201.6000 8201.7200 32095.2000 28298.2000
## 417 27999.6000 25579.9000 36579.5000 24230.0000 35737.5000
## 418 33156.0000 25090.5000 21724.8000 33961.5000 32207.9000
## 419 36985.8000 29108.4000 42172.6000 26561.9000 26942.1000
## 420 43955.5000 19315.7000 22908.6000 25997.5000 32441.5000
## 421 32234.4000 19889.2000 19315.7000 52554.1000 26827.3000
## 422 31260.5000 42857.9000 30886.2000 23620.5000 23925.2000
## 423 190240.0000 1001.2600 81154.8000 58918.8000 52018.0000
## 424 37602.4000 22666.8000 69510.5000 43960.8000 37742.7000
## 425 12815.8000 21102.9000 25227.5000 28629.0000 34831.3000
## 426 30468.3000 27192.7000 24373.2000 16403.4000 23855.6000
## 427 63547.0000 88843.5000 56608.2000 57637.6000 71765.0000
## 428 62953.1000 57562.9000 37529.9000 45885.5000 17740.3000
## 429 18769.8000 21377.4000 16627.6000 16751.7000 8746.3200
## 430 24362.3000 21849.8000 12877.1000 12821.7000 11105.4000
## 431 53649.0000 69751.1000 40856.5000 26544.8000 5109.9700
## 432 22629.5000 19094.4000 19411.2000 8575.0300 8201.7200
## 433 22711.9000 23210.1000 21911.4000 21817.0000 18524.3000
## 434 31285.6000 20857.0000 25995.0000 15448.8000 17635.2000
## 435 19391.7000 34241.6000 14600.1000 26827.3000 40259.4000
## 436 109308.0000 60337.9000 72999.6000 64308.2000 67047.8000
## 437 38819.7000 33371.3000 16500.2000 12542.0000 15040.4000
## 438 30424.8000 45430.0000 88936.8000 60078.4000 63766.7000
## 439 60826.0000 127793.0000 123282.0000 75596.7000 77178.0000
## 440 62918.2000 69582.5000 74184.7000 30227.6000 66890.9000
## 441 83053.6000 37483.6000 48422.1000 99220.4000 70866.1000
## 442 53674.6000 90141.1000 60998.1000 61150.2000 69463.8000
## 443 71889.4000 80271.0000 169836.0000 57536.1000 56839.4000
## 444 53962.5000 84534.2000 49897.9000 137284.0000 28212.5000
## 445 68923.0000 71032.2000 71810.0000 74127.8000 44083.4000
## 446 71770.9000 52895.9000 67689.0000 89762.5000 70925.4000
## 447 118946.0000 61885.0000 113687.0000 51908.5000 218212.0000
## 448 28704.2000 49214.1000 40597.3000 80511.8000 26817.5000
## 449 72957.6000 83141.0000 81488.7000 96728.4000 76987.7000
## 450 38092.1000 57432.5000 54809.8000 41996.8000 50495.7000
## 451 40556.6000 26450.1000 34321.0000 82712.9000 62527.1000
## 452 38218.9000 64417.8000 70974.1000 6864.1900 59448.1000
## 453 18874.4000 49443.3000 46295.1000 14949.6000 70751.7000
## 454 60146.7000 90988.4000 58666.4000 45995.3000 62415.5000
## 455 82902.6000 49816.5000 54452.6000 58090.6000 47882.0000
## 456 77862.8000 31022.2000 47654.1000 31540.5000 36443.1000
## 457 50120.0000 83954.4000 40204.0000 27144.1000 19761.3000
## 458 31680.9000 54526.6000 39618.8000 97154.8000 40556.6000
## 459 65250.4000 35265.0000 42241.2000 52490.3000 57527.7000
## 460 64998.5000 48307.1000 45187.0000 27603.2000 54282.3000
## 461 32102.3000 73731.7000 58280.4000 44920.7000 70754.0000
## 462 61179.5000 123048.0000 51790.2000 85621.1000 21120.6000
## 463 38050.0000 45049.3000 105987.0000 48606.4000 122831.0000
## 464 50193.1000 50828.3000 32242.8000 30346.5000 43426.2000
## 465 55018.4000 54932.5000 96502.0000 15572.5000 89202.2000
## 466 51503.7000 70150.1000 97839.1000 61894.6000 50695.8000
## 467 66523.7000 48257.3000 35175.3000 36804.3000 41524.9000
## 468 61252.0000 84722.4000 90960.3000 78522.6000 47168.9000
## 469 68498.9000 77786.4000 82730.7000 92152.4000 50024.5000
## 470 54638.9000 25954.2000 41350.3000 48994.6000 61542.4000
## 471 176727.0000 52394.6000 72648.6000 53737.5000 68658.3000
## 472 75607.5000 87423.6000 88210.7000 98889.0000 67832.9000
## 473 85730.0000 87654.3000 91187.8000 80301.6000 81113.3000
## 474 78263.0000 78198.4000 69010.6000 81113.3000 78398.4000
## 475 64047.8000 54289.5000 82948.0000 67481.0000 41447.5000
## 476 87648.6000 86179.9000 130497.0000 106866.0000 31145.1000
## 477 61636.2000 87697.6000 45944.0000 121743.0000 147844.0000
## 478 42578.5000 31139.2000 48668.0000 51547.0000 27001.2000
## 479 40775.9000 25542.9000 22306.2000 43825.2000 48577.4000
## 480 23513.9000 32299.2000 42241.2000 34441.4000 66692.3000
## 481 92698.9000 191442.0000 83509.4000 95077.8000 49105.4000
## 482 64378.1000 47305.0000 30227.6000 45476.7000 69662.2000
## 483 39977.0000 63901.0000 60191.9000 53721.4000 53068.4000
## 484 34654.7000 35782.0000 269757.0000 22374.7000 39702.5000
## 485 14231.2000 44616.2000 57214.4000 70721.6000 28682.0000
## 486 28294.8000 29489.4000 38608.7000 35565.8000 57422.6000
## 487 52773.6000 55486.3000 39676.1000 38140.2000 41878.5000
## 488 30227.6000 23174.5000 43838.9000 37006.2000 25597.7000
## 489 29877.0000 51908.5000 31132.1000 40892.8000 35329.3000
## 490 22894.3000 15661.1000 27623.1000 52274.4000 41290.8000
## 491 47909.3000 39470.9000 20492.3000 125798.0000 42739.5000
## 492 55086.5000 43930.7000 55701.6000 47151.0000 40035.4000
## 493 30819.3000 18513.9000 22003.7000 18999.3000 20328.6000
## 494 38239.5000 45025.6000 43551.7000 60495.4000 34241.1000
## 495 28735.3000 33348.9000 27879.7000 40268.3000 25215.9000
## 496 39957.9000 38017.1000 3022.7600 34281.4000 16464.3000
## 497 22469.0000 17029.2000 50608.2000 20790.2000 17179.3000
## 498 13113.4000 50379.4000 64485.6000 35736.1000 25794.3000
## 499 37961.6000 29961.4000 38153.2000 26359.2000 24179.8000
## 500 85035.8000 11419.9000 19560.2000 27800.1000 30367.0000
## 501 5190.8500 25878.3000 27452.5000 9530.4500 28396.1000
## 502 71755.1000 61806.0000 25439.6000 45679.5000 25954.2000
## 503 42803.7000 32556.0000 41526.8000 26848.6000 30445.5000
## 504 47185.2000 54097.8000 26790.3000 32956.1000 20195.6000
## 505 25637.5000 37848.1000 31718.6000 61340.5000 17523.1000
## 506 31770.8000 64417.8000 44750.5000 62848.6000 172133.0000
## 507 54148.9000 38790.4000 31147.5000 105152.0000 120911.0000
## 508 75617.1000 83228.0000 215572.0000 167285.0000 42812.4000
## 509 88528.8000 118948.0000 84965.8000 22961.4000 61125.4000
## 510 67090.5000 61894.4000 72824.2000 81779.1000 56296.6000
## 511 54982.9000 61340.5000 54706.1000 55170.0000 48979.0000
## 512 37950.8000 48708.2000 96691.1000 72591.8000 92895.8000
## 513 66105.3000 64022.3000 57312.8000 132799.0000 151276.0000
## 514 25347.9000 46459.3000 8060.7000 7041.6700 90838.7000
## 515 42241.2000 52781.9000 39738.9000 33337.0000 45592.9000
## 516 49469.7000 49755.9000 42288.3000 37925.1000 45811.3000
## 517 19079.6000 42656.9000 23329.0000 50094.8000 27246.2000
## 518 33138.9000 39337.4000 33360.3000 34104.0000 53779.4000
## 519 36707.5000 50346.9000 35358.0000 60727.4000 20379.4000
## 520 23300.7000 34424.9000 50728.7000 26620.9000 21902.5000
## 521 45183.9000 31076.3000 36191.0000 26120.3000 25865.4000
## 522 29865.6000 26134.4000 35681.4000 26109.4000 22063.8000
## 523 26285.2000 29110.5000 28233.6000 26576.0000 16598.1000
## 524 19196.0000 20769.6000 31299.3000 12091.1000 28212.1000
## 525 22199.0000 21912.7000 27046.4000 51189.9000 60830.1000
## 526 101741.0000 68931.5000 80443.5000 87554.5000 74923.8000
## 527 83603.0000 67737.2000 56459.2000 96888.4000 40303.5000
## 528 35038.8000 34282.2000 36899.6000 40009.6000 69474.8000
## 529 23513.9000 64015.6000 42241.2000 54347.3000 59006.8000
## 530 55917.4000 39900.2000 40552.8000 47871.9000 87664.4000
## 531 31301.3000 11470.7000 10255.4000 11245.8000 4665.1100
## 532 14456.7000 19875.2000 27603.2000 18136.6000 3003.8200
## 533 28317.8000 51908.5000 28821.5000 28870.0000 29665.1000
## 534 17892.4000 18316.3000 18080.8000 10736.3000 23159.0000
## 535 15255.8000 12091.1000 16122.2000 12636.9000 26721.2000
## 536 11744.9000 9043.9300 21259.9000 11169.3000 13214.9000
## 537 15899.7000 20196.3000 15080.2000 18705.3000 14160.5000
## 538 17163.3000 11908.8000 13140.0000 2053.2000 20278.3000
## 539 13931.2000 8517.3200 12711.0000 17349.6000 9816.3400
## 540 28943.8000 32736.9000 25732.7000 40469.3000 38779.2000
## 541 18364.3000 35567.1000 25954.2000 10188.2000 20299.4000
## 542 19640.0000 20283.4000 18530.7000 20602.9000 36532.4000
## 543 19006.2000 18365.8000 25145.3000 20999.4000 22400.3000
## 544 28669.0000 42915.7000 26465.9000 40972.7000 31943.7000
## 545 16542.2000 18426.8000 39541.7000 33243.1000 44039.6000
## 546 3325.0400 7778.5500 2561.6900 16004.2000 14604.2000
## 547 36419.0000 26469.8000 12207.8000 12268.1000 25201.9000
## 548 17129.0000 23212.7000 4562.6200 22149.3000 18512.9000
## 549 22485.1000 19665.0000 21378.6000 24409.5000 25053.9000
## 550 3857.6200 9200.3800 12796.4000 10833.5000 9450.3700
## 551 13812.0000 24218.5000 21273.8000 4637.2500 22974.7000
## 552 3054.3900 18637.0000 15507.6000 23436.9000 7786.2700
## 553 42018.3000 24282.3000 43849.3000 42862.6000 76344.2000
## 554 13340.4000 10846.7000 6458.9300 18017.6000 15859.6000
## 555 33123.3000 24701.2000 32714.9000 10771.5000 56453.7000
## 556 23908.2000 19157.1000 57179.2000 41897.8000 34452.4000
## 557 44662.1000 36306.8000 93053.7000 40589.9000 114199.0000
## 558 30142.2000 24887.0000 42024.1000 65171.9000 40831.0000
## 559 127594.0000 62776.9000 63843.4000 47602.7000 19700.2000
## 560 26604.8000 43069.9000 44574.1000 53290.6000 52077.7000
## 561 18191.2000 16706.4000 26608.5000 28258.2000 43187.9000
## 562 70531.2000 48352.1000 35487.1000 22742.2000 27768.6000
## 563 39811.0000 24749.7000 26068.6000 9888.6600 25021.2000
## 564 58440.1000 34201.9000 33132.8000 33104.4000 3244.5300
## 565 30586.5000 40129.1000 35907.0000 23522.7000 33833.6000
## 566 26580.9000 37983.1000 36917.9000 45077.1000 20971.1000
## 567 23696.8000 25705.8000 24434.6000 36961.1000 22321.6000
## 568 34819.6000 24334.0000 43603.1000 36433.5000 34011.1000
## 569 30417.5000 28297.1000 23975.6000 25433.1000 17025.3000
## 570 49991.0000 42241.2000 24610.8000 27263.7000 40464.8000
## 571 11998.5000 20730.9000 18098.5000 15462.2000 28739.2000
## 572 19865.5000 19141.0000 25347.9000 19960.9000 21471.1000
## 573 45623.6000 52522.4000 41255.5000 26509.0000 26872.7000
## 574 22250.1000 23851.9000 23717.1000 18895.5000 37344.4000
## 575 33877.4000 39734.1000 28268.8000 23816.2000 24160.6000
## 576 38288.3000 50379.4000 33054.0000 34135.5000 10945.3000
## 577 14912.7000 6681.9300 34833.0000 33351.7000 38992.0000
## 578 42374.7000 52024.5000 48634.1000 48668.0000 57117.6000
## 579 46129.5000 40213.6000 46813.6000 50023.8000 27367.5000
## 580 23662.9000 21729.3000 19903.7000 19546.2000 15827.0000
## 581 22491.5000 34203.6000 30670.3000 15335.1000 31686.3000
## 582 37958.3000 28736.1000 38955.3000 35564.4000 35076.3000
## 583 49681.9000 20763.4000 21474.4000 42232.7000 26046.8000
## 584 27691.0000 19762.4000 44641.3000 17578.7000 20479.0000
## 585 25230.2000 22013.5000 27399.4000 20679.6000 23283.7000
## 586 31967.5000 35550.1000 30655.1000 60042.8000 15840.5000
## 587 32585.0000 36033.7000 39852.9000 24091.6000 30227.6000
## 588 18198.4000 23593.3000 14909.2000 15894.2000 29482.7000
## 589 12458.0000 24325.5000 7690.8000 16046.1000 25558.6000
## 590 30301.9000 63923.7000 33983.9000 37430.0000 35674.1000
## 591 21956.6000 15845.5000 27392.1000 24482.7000 29869.3000
## 592 36993.4000 15048.0000 28628.8000 23608.1000 21422.3000
## 593 55417.3000 37519.2000 37790.1000 15943.4000 24286.8000
## 594 15625.5000 31258.1000 38852.3000 36431.2000 22672.5000
## 595 52934.9000 53451.1000 30275.6000 22639.2000 15447.7000
## 596 20312.5000 36375.4000 35585.5000 38518.0000 29347.3000
## 597 25863.9000 27856.2000 26357.0000 14796.5000 24035.8000
## 598 36218.9000 5190.8500 16849.6000 60455.3000 18142.4000
## 599 14756.2000 27192.8000 50171.7000 29767.4000 45439.6000
## 600 27365.7000 23280.8000 32856.0000 26090.6000 29323.6000
## 601 41446.7000 45204.1000 67481.0000 72546.3000 43770.5000
## 602 54570.9000 39400.8000 163575.0000 51309.0000 86899.1000
## 603 52390.8000 43490.2000 121670.0000 95609.1000 45085.3000
## 604 51317.4000 45791.4000 31018.6000 48837.8000 40893.0000
## 605 22447.9000 60455.3000 60455.3000 11252.3000 34575.8000
## 606 9165.0600 28829.6000 35011.9000 24013.5000 41526.8000
## 607 59389.8000 44923.6000 61936.4000 30833.6000 44760.6000
## 608 35828.5000 11627.5000 32036.3000 31126.3000 33142.3000
## 609 25772.4000 30732.0000 42477.1000 38830.6000 50592.1000
## 610 53263.0000 29352.5000 35155.7000 32207.7000 29303.2000
## 611 101.3920 20555.8000 25465.1000 36961.1000 50508.2000
## 612 15206.8000 35744.6000 31444.0000 32898.7000 34400.5000
## 613 43415.1000 43570.8000 43749.7000 37737.9000 34551.1000
## 614 43555.4000 38859.2000 38017.1000 86272.9000 40150.3000
## 615 32461.7000 23924.5000 65254.9000 31650.1000 84111.3000
## 616 37794.2000 25153.7000 38951.2000 34966.1000 35905.0000
## 617 31116.7000 24685.9000 35156.7000 19656.2000 38092.1000
## 618 56329.3000 48231.9000 27384.0000 23581.4000 29241.0000
## 619 26835.1000 23495.8000 37957.7000 15840.5000 27215.4000
## 620 9725.0000 12786.5000 20820.9000 29069.8000 25126.5000
## 621 49832.2000 23181.0000 25521.2000 23550.8000 24254.8000
## 622 22873.3000 43712.5000 33570.1000 54409.8000 47756.7000
## 623 26302.5000 31757.4000 30048.9000 45604.5000 31095.6000
## 624 39458.5000 11280.2000 50695.8000 30519.5000 10381.7000
## 625 33206.7000 29647.9000 30332.4000 26637.6000 29025.0000
## 626 18136.6000 29771.3000 29543.8000 28727.8000 31045.2000
## 627 20846.3000 16628.3000 18379.4000 5725.1200 18839.8000
## 628 19236.6000 29664.3000 15341.5000 21221.8000 13441.8000
## 629 20028.1000 7097.4100 40344.4000 2027.8300 30459.6000
## 630 30096.9000 24104.5000 9611.0600 18482.9000 14784.4000
## 631 12887.5000 39073.1000 23319.0000 26361.8000 25954.2000
## 632 86894.6000 100817.0000 72671.9000 70333.6000 47099.9000
## 633 56779.3000 58475.8000 51549.5000 34108.3000 50161.7000
## 634 18168.0000 33560.0000 23672.1000 26580.9000 26963.6000
## 635 24825.2000 33221.4000 19417.1000 17379.8000 32382.9000
## 636 37017.4000 42813.4000 33881.8000 21366.8000 22306.2000
## 637 29767.2000 40180.8000 26161.2000 29399.7000 22166.9000
## 638 24818.3000 40159.8000 29648.0000 21913.6000 22176.6000
## 639 36561.0000 25761.9000 3169.2600 33855.0000 32262.4000
## 640 24619.2000 22472.0000 14100.8000 29792.4000 32931.7000
## 641 37960.2000 48167.4000 33390.6000 120657.0000 93926.3000
## 642 124580.0000 38546.5000 66578.9000 114709.0000 47034.2000
## 643 25400.9000 8616.8100 23688.3000 27880.5000 12561.5000
## 644 30309.2000 26017.2000 26664.6000 36715.5000 33961.3000
## 645 18292.4000 23509.0000 8305.3600 75947.2000 67909.6000
## 646 67113.6000 31827.5000 60147.0000 60733.5000 54090.9000
## 647 15610.1000 13597.5000 23430.3000 18962.4000 19838.7000
## 648 40129.1000 57099.5000 64417.8000 25266.5000 21448.6000
## 649 20465.0000 48701.9000 45365.3000 36961.1000 41222.6000
## 650 38776.3000 30764.6000 22345.6000 20173.3000 19603.6000
## 651 21716.1000 13817.2000 19461.6000 22421.3000 6906.4600
## 652 17892.3000 16569.7000 16510.0000 21120.6000 26727.3000
## 653 1557.2600 32672.8000 22891.3000 31635.4000 20059.7000
## 654 49633.4000 38849.0000 65168.7000 64537.4000 69915.1000
## 655 52801.5000 33793.0000 18097.2000 10900.8000 13675.3000
## 656 23506.0000 43111.0000
## Nov Dec
## 1 26041.3000 67608.9000
## 2 81923.0000 27905.0000
## 3 100842.0000 28418.6000
## 4 136108.0000 20226.3000
## 5 78688.6000 55689.0000
## 6 113560.0000 97502.7000
## 7 8499.5100 20686.9000
## 8 5573.6800 32824.1000
## 9 69671.0000 66425.7000
## 10 48233.7000 84248.5000
## 11 11264.4000 25311.6000
## 12 23891.8000 16477.8000
## 13 28124.7000 34415.1000
## 14 36847.1000 22532.8000
## 15 34299.6000 36556.0000
## 16 10077.1000 10217.6000
## 17 32752.2000 36196.7000
## 18 28097.7000 29580.5000
## 19 76397.5000 72095.7000
## 20 40730.7000 28921.6000
## 21 68908.1000 31576.6000
## 22 33465.4000 47161.9000
## 23 54995.0000 43800.5000
## 24 70642.1000 32269.6000
## 25 22633.8000 30889.7000
## 26 14651.2000 12937.7000
## 27 33888.8000 24652.1000
## 28 44548.7000 41573.5000
## 29 96550.0000 102796.0000
## 30 10881.4000 11673.5000
## 31 78124.0000 166559.0000
## 32 7082.9800 8192.3000
## 33 44764.3000 46306.0000
## 34 14515.1000 13430.2000
## 35 85769.5000 53026.4000
## 36 50196.2000 57480.8000
## 37 95665.5000 104634.0000
## 38 80542.0000 18632.0000
## 39 42906.6000 27898.0000
## 40 22610.6000 41332.8000
## 41 37889.4000 37012.2000
## 42 35091.7000 22448.0000
## 43 22645.9000 71965.1000
## 44 34267.7000 25587.8000
## 45 26327.0000 22100.1000
## 46 10790.1000 34957.1000
## 47 6997.2900 9494.5800
## 48 14786.6000 22492.5000
## 49 29333.8000 46247.9000
## 50 3958.2900 33640.3000
## 51 28348.7000 25418.8000
## 52 16222.1000 27255.1000
## 53 25619.0000 32261.2000
## 54 160879.0000 114335.0000
## 55 35985.5000 66964.3000
## 56 42975.4000 43281.5000
## 57 11362.1000 12644.0000
## 58 31719.3000 24584.8000
## 59 53984.2000 45507.8000
## 60 39034.2000 33643.7000
## 61 49356.0000 109414.0000
## 62 60328.1000 47157.8000
## 63 46660.2000 123330.0000
## 64 24000.8000 24776.0000
## 65 32916.3000 5208.2700
## 66 35776.2000 40699.5000
## 67 24540.8000 10704.5000
## 68 35237.6000 46198.3000
## 69 46203.4000 21941.2000
## 70 43054.1000 24959.7000
## 71 36809.7000 30323.3000
## 72 25713.9000 25874.5000
## 73 52438.1000 101924.0000
## 74 59788.3000 65491.8000
## 75 57255.6000 65129.3000
## 76 58332.6000 56257.7000
## 77 21967.1000 24389.1000
## 78 33451.6000 27484.4000
## 79 57238.8000 50711.8000
## 80 27721.1000 65213.6000
## 81 39658.9000 41613.5000
## 82 31224.8000 21504.8000
## 83 38099.0000 29721.3000
## 84 43373.6000 37370.1000
## 85 21998.9000 25210.6000
## 86 15283.3000 31991.8000
## 87 46854.1000 36720.7000
## 88 34830.1000 58009.2000
## 89 14390.1000 27353.4000
## 90 3470.3200 91408.7000
## 91 19379.8000 54426.6000
## 92 22013.6000 19168.2000
## 93 25624.7000 34539.3000
## 94 24987.6000 22484.7000
## 95 30692.8000 32972.7000
## 96 17713.0000 27637.0000
## 97 50421.2000 26796.5000
## 98 9959.9100 10824.7000
## 99 25830.9000 25993.0000
## 100 46422.6000 85342.6000
## 101 71697.0000 68427.7000
## 102 51807.6000 28182.7000
## 103 37276.5000 23005.0000
## 104 37320.6000 19498.7000
## 105 61442.2000 34016.3000
## 106 65515.7000 46175.3000
## 107 20691.4000 16554.0000
## 108 32597.9000 26259.3000
## 109 21261.6000 18880.6000
## 110 40677.7000 37499.5000
## 111 19920.7000 24083.7000
## 112 16037.7000 18677.8000
## 113 35874.5000 29264.5000
## 114 50865.7000 37620.5000
## 115 28896.1000 16409.9000
## 116 41055.6000 15797.5000
## 117 44600.5000 6088.0600
## 118 35087.2000 52430.5000
## 119 73615.0000 30721.1000
## 120 75495.3000 72072.4000
## 121 71247.0000 76462.6000
## 122 26831.3000 26502.0000
## 123 50817.7000 82880.0000
## 124 33987.3000 19088.1000
## 125 21330.0000 18757.6000
## 126 15597.0000 20285.2000
## 127 10416.5000 57596.0000
## 128 11613.1000 15809.7000
## 129 27967.0000 41659.0000
## 130 49235.9000 50421.2000
## 131 10663.8000 35556.5000
## 132 52831.7000 38130.5000
## 133 32986.9000 27530.0000
## 134 48233.7000 1024.0400
## 135 63100.4000 38754.3000
## 136 25998.5000 26100.2000
## 137 18739.9000 30796.2000
## 138 44648.0000 31911.8000
## 139 54385.3000 44985.8000
## 140 27318.2000 21644.3000
## 141 30197.8000 10240.4000
## 142 34971.6000 47469.0000
## 143 16100.2000 21839.8000
## 144 60109.8000 40924.8000
## 145 18347.3000 12101.1000
## 146 74149.9000 34434.5000
## 147 54582.7000 63804.6000
## 148 18878.9000 26455.1000
## 149 48709.6000 42966.1000
## 150 48055.2000 16692.3000
## 151 43332.8000 41961.1000
## 152 15360.6000 49168.8000
## 153 48320.2000 28235.2000
## 154 22424.9000 44308.0000
## 155 16765.6000 17799.8000
## 156 512.0180 24035.0000
## 157 32151.4000 19866.3000
## 158 19192.7000 12441.8000
## 159 31163.5000 38025.4000
## 160 17261.1000 54706.8000
## 161 117913.0000 53893.1000
## 162 156341.0000 109414.0000
## 163 41666.2000 16345.9000
## 164 11045.6000 18035.2000
## 165 39504.2000 31764.5000
## 166 86179.0000 118310.0000
## 167 42395.1000 36790.0000
## 168 58064.7000 59317.7000
## 169 53498.9000 56946.9000
## 170 45211.2000 42693.2000
## 171 49656.6000 46678.5000
## 172 16848.1000 18151.7000
## 173 10240.4000 30721.1000
## 174 53767.1000 72936.3000
## 175 80861.2000 72213.0000
## 176 61466.0000 39890.4000
## 177 38928.7000 34282.9000
## 178 49603.0000 45907.2000
## 179 84180.6000 74119.0000
## 180 57578.2000 60100.2000
## 181 21437.8000 22462.2000
## 182 17408.6000 22476.7000
## 183 57877.5000 57078.2000
## 184 39209.6000 29473.5000
## 185 65781.8000 38026.3000
## 186 7038.1500 22304.1000
## 187 25724.7000 20706.0000
## 188 106257.0000 60076.9000
## 189 47980.4000 26827.3000
## 190 32672.5000 36939.8000
## 191 39270.1000 100191.0000
## 192 26987.3000 25461.6000
## 193 14191.8000 19057.6000
## 194 48847.1000 28485.3000
## 195 33100.5000 32852.9000
## 196 32816.5000 26821.3000
## 197 40762.0000 41364.0000
## 198 49286.1000 53971.1000
## 199 26607.1000 24649.2000
## 200 19320.7000 15696.3000
## 201 16412.0000 42896.4000
## 202 46298.4000 21307.3000
## 203 36865.3000 24447.0000
## 204 23033.3000 6437.3700
## 205 11716.7000 47174.0000
## 206 29864.6000 22076.9000
## 207 38741.4000 26698.8000
## 208 23359.9000 39772.6000
## 209 33223.9000 31291.9000
## 210 83605.2000 55812.5000
## 211 26867.3000 39582.9000
## 212 48296.4000 45347.2000
## 213 9279.6300 50970.5000
## 214 104688.0000 80115.8000
## 215 10173.5000 15956.5000
## 216 33008.7000 80299.1000
## 217 22612.1000 42353.9000
## 218 13340.6000 24289.6000
## 219 36457.9000 33800.1000
## 220 139449.0000 96560.6000
## 221 45715.2000 87190.1000
## 222 64513.5000 82017.2000
## 223 56782.4000 84249.0000
## 224 58687.6000 74336.1000
## 225 44674.7000 45951.6000
## 226 69482.9000 36853.9000
## 227 33862.9000 121492.0000
## 228 45948.9000 55075.9000
## 229 29719.2000 46847.4000
## 230 36118.9000 22239.8000
## 231 62049.3000 46420.9000
## 232 62573.6000 40050.6000
## 233 51855.4000 36239.2000
## 234 19782.0000 16171.2000
## 235 37542.7000 55005.9000
## 236 49173.7000 81933.0000
## 237 43953.9000 76891.1000
## 238 345184.0000 48687.8000
## 239 41089.4000 13353.8000
## 240 74107.6000 39457.1000
## 241 48347.1000 55500.6000
## 242 29036.7000 50801.7000
## 243 55377.9000 74174.2000
## 244 50479.2000 62892.7000
## 245 41496.0000 52736.8000
## 246 80284.6000 45403.5000
## 247 85814.7000 51028.9000
## 248 99191.0000 74099.8000
## 249 71624.8000 127390.0000
## 250 56255.8000 53118.2000
## 251 41505.0000 21113.4000
## 252 64992.0000 92935.4000
## 253 26030.1000 69607.4000
## 254 64953.2000 64575.5000
## 255 10730.9000 74038.1000
## 256 86915.0000 84112.0000
## 257 64792.0000 80101.1000
## 258 72033.5000 76096.5000
## 259 70744.5000 110007.0000
## 260 43051.3000 46304.8000
## 261 57155.3000 53471.1000
## 262 39613.2000 71734.2000
## 263 50216.7000 60460.0000
## 264 28035.4000 49210.3000
## 265 36004.6000 48883.3000
## 266 57118.6000 70068.4000
## 267 33225.4000 38393.2000
## 268 57702.2000 42746.3000
## 269 27682.2000 41684.6000
## 270 35596.8000 33436.8000
## 271 53654.7000 31091.3000
## 272 41118.0000 31635.6000
## 273 39378.6000 121384.0000
## 274 19923.4000 41432.9000
## 275 34339.0000 30553.7000
## 276 38604.6000 23509.1000
## 277 31809.2000 25314.1000
## 278 11856.3000 47127.8000
## 279 16796.3000 45296.5000
## 280 51663.9000 64846.9000
## 281 23948.4000 12255.1000
## 282 27993.5000 47282.8000
## 283 45109.5000 25630.4000
## 284 41008.6000 30372.3000
## 285 32046.9000 34614.8000
## 286 24338.6000 60575.1000
## 287 35457.0000 20191.7000
## 288 95850.7000 25754.3000
## 289 186829.0000 40246.3000
## 290 54727.8000 12463.4000
## 291 68225.0000 57847.9000
## 292 53673.3000 57561.6000
## 293 98915.9000 69256.1000
## 294 136840.0000 149710.0000
## 295 98420.6000 72879.4000
## 296 43039.7000 20857.0000
## 297 51508.5000 36410.3000
## 298 30198.6000 36930.9000
## 299 55653.9000 57570.1000
## 300 28773.1000 53186.1000
## 301 25297.6000 21868.7000
## 302 19630.2000 30287.6000
## 303 19343.6000 27057.9000
## 304 26799.8000 24199.0000
## 305 26687.0000 27603.7000
## 306 56763.4000 35044.2000
## 307 86161.9000 104572.0000
## 308 60644.5000 68112.5000
## 309 72552.2000 83784.2000
## 310 56755.3000 30169.1000
## 311 26775.2000 24983.6000
## 312 13936.2000 3962.8400
## 313 20992.0000 35641.7000
## 314 14728.2000 20504.3000
## 315 15967.2000 13515.5000
## 316 18172.5000 27403.3000
## 317 8958.7900 8863.5300
## 318 17893.0000 16663.9000
## 319 17475.6000 17925.9000
## 320 11804.0000 15708.2000
## 321 38324.1000 31452.5000
## 322 24147.3000 16034.3000
## 323 25882.0000 52065.7000
## 324 55271.1000 23269.5000
## 325 28911.8000 32192.8000
## 326 28791.5000 52941.9000
## 327 17048.8000 20980.1000
## 328 26022.4000 19719.7000
## 329 19213.1000 40267.6000
## 330 25215.7000 24747.9000
## 331 10025.1000 19943.1000
## 332 25730.4000 17428.7000
## 333 7689.1100 15870.5000
## 334 74548.7000 49566.5000
## 335 18351.8000 21063.8000
## 336 10637.1000 53731.8000
## 337 56005.6000 35699.7000
## 338 42148.7000 85487.7000
## 339 23530.5000 44672.8000
## 340 56234.7000 22856.9000
## 341 19541.5000 46066.3000
## 342 18092.9000 19987.7000
## 343 48810.9000 35044.2000
## 344 24347.7000 17629.4000
## 345 32125.1000 33495.8000
## 346 15023.3000 30870.8000
## 347 26606.4000 14301.5000
## 348 19315.7000 65623.0000
## 349 33890.6000 37031.2000
## 350 32966.1000 50303.8000
## 351 23808.8000 21018.2000
## 352 807.6680 25359.9000
## 353 45388.0000 32806.9000
## 354 38966.0000 35116.2000
## 355 21827.8000 22099.6000
## 356 38784.4000 44168.1000
## 357 29596.8000 22088.6000
## 358 27271.8000 7299.9600
## 359 42237.4000 51799.9000
## 360 63140.2000 43472.2000
## 361 16965.4000 21643.8000
## 362 31816.2000 22210.9000
## 363 31721.4000 26863.6000
## 364 22768.7000 28814.5000
## 365 40489.2000 29594.7000
## 366 18242.6000 27724.8000
## 367 27493.7000 29176.4000
## 368 30187.3000 31448.4000
## 369 16836.3000 24857.1000
## 370 43281.5000 64993.0000
## 371 18979.9000 53054.1000
## 372 28222.3000 30875.5000
## 373 23854.1000 27412.6000
## 374 22556.1000 22062.9000
## 375 26438.3000 2120.1300
## 376 29084.6000 20978.2000
## 377 512.6070 21600.8000
## 378 25274.9000 21477.2000
## 379 36291.4000 42634.8000
## 380 19275.7000 21720.0000
## 381 70492.0000 63903.7000
## 382 28031.1000 30231.2000
## 383 12962.7000 49380.0000
## 384 31181.7000 17581.8000
## 385 44713.5000 40229.3000
## 386 48000.1000 40383.4000
## 387 32106.1000 39049.3000
## 388 16853.2000 14353.0000
## 389 44304.3000 54960.6000
## 390 30464.3000 27079.2000
## 391 38061.3000 42752.1000
## 392 32192.8000 48249.8000
## 393 34317.9000 429542.0000
## 394 40993.3000 48215.8000
## 395 42693.9000 61512.9000
## 396 40414.2000 55519.8000
## 397 33110.5000 30931.5000
## 398 7726.2800 19006.7000
## 399 53654.7000 56945.0000
## 400 46231.7000 6207.8400
## 401 21600.1000 21964.2000
## 402 21186.2000 25681.9000
## 403 31418.5000 29298.8000
## 404 34922.0000 28168.9000
## 405 40603.3000 30232.8000
## 406 55069.5000 35178.5000
## 407 8381.9700 25724.8000
## 408 12619.8000 21713.1000
## 409 26508.1000 14074.6000
## 410 20052.4000 36907.7000
## 411 15019.0000 27716.9000
## 412 13065.4000 38585.5000
## 413 84919.8000 87181.3000
## 414 54317.7000 57840.3000
## 415 34404.2000 23129.8000
## 416 32806.9000 23296.5000
## 417 42960.0000 30612.8000
## 418 30289.5000 24687.4000
## 419 39884.8000 24290.7000
## 420 24932.8000 11869.7000
## 421 18201.2000 14766.1000
## 422 49886.9000 45138.1000
## 423 78951.5000 64669.1000
## 424 31868.6000 28315.4000
## 425 34160.8000 36847.3000
## 426 27493.3000 26862.5000
## 427 25512.3000 33946.1000
## 428 90113.8000 55271.2000
## 429 16248.0000 63289.1000
## 430 10016.3000 29537.2000
## 431 13617.2000 28373.5000
## 432 21445.7000 10252.2000
## 433 24228.4000 14780.4000
## 434 43924.7000 28451.3000
## 435 36703.0000 36917.0000
## 436 54228.3000 60075.8000
## 437 24190.3000 47146.8000
## 438 27908.7000 77908.7000
## 439 89159.6000 73620.7000
## 440 63515.9000 82490.5000
## 441 10223.4000 101005.0000
## 442 60138.0000 65473.9000
## 443 50879.0000 46898.3000
## 444 77419.4000 60258.7000
## 445 42645.4000 60085.7000
## 446 63545.1000 64575.0000
## 447 76473.8000 82160.2000
## 448 100702.0000 60484.1000
## 449 60220.2000 86230.0000
## 450 61739.0000 44369.4000
## 451 67040.1000 46099.3000
## 452 78994.9000 32714.9000
## 453 47152.8000 38205.1000
## 454 49476.3000 55023.0000
## 455 43429.2000 56779.3000
## 456 84512.9000 54644.9000
## 457 49681.9000 37936.3000
## 458 72546.3000 56204.9000
## 459 41193.5000 61015.0000
## 460 75702.4000 55622.7000
## 461 58268.8000 69565.6000
## 462 100253.0000 66889.4000
## 463 69577.5000 51817.9000
## 464 31128.4000 38115.8000
## 465 121525.0000 44369.6000
## 466 78382.7000 81603.8000
## 467 20278.3000 34309.8000
## 468 87133.5000 51699.5000
## 469 97215.1000 73738.1000
## 470 48484.3000 40232.7000
## 471 56472.5000 74037.8000
## 472 55102.1000 54179.4000
## 473 58571.6000 91190.7000
## 474 48371.1000 149769.0000
## 475 81895.2000 73995.7000
## 476 90682.9000 89626.7000
## 477 89242.5000 80210.9000
## 478 47483.2000 51655.6000
## 479 23533.9000 29216.8000
## 480 47049.5000 79967.2000
## 481 52801.5000 92561.1000
## 482 43555.3000 59493.1000
## 483 81862.9000 72110.6000
## 484 38448.5000 89142.7000
## 485 29779.8000 54850.7000
## 486 10939.1000 31214.5000
## 487 37904.2000 36273.2000
## 488 44000.9000 28855.9000
## 489 34340.5000 36133.3000
## 490 138830.0000 81248.2000
## 491 28861.1000 33942.2000
## 492 79109.9000 54244.0000
## 493 41206.7000 40415.0000
## 494 38731.5000 42558.1000
## 495 10501.8000 34757.0000
## 496 16315.7000 22536.0000
## 497 44132.2000 30226.9000
## 498 36854.9000 8007.2100
## 499 55774.2000 65853.8000
## 500 25551.5000 30147.8000
## 501 28152.1000 42597.7000
## 502 346714.0000 68750.9000
## 503 51853.3000 57942.5000
## 504 33411.0000 13232.5000
## 505 41145.4000 43949.4000
## 506 132263.0000 166484.0000
## 507 68873.3000 70974.1000
## 508 219873.0000 128180.0000
## 509 49793.0000 64974.5000
## 510 34504.2000 17186.2000
## 511 55934.1000 29647.9000
## 512 100759.0000 21946.9000
## 513 95427.3000 26197.3000
## 514 89646.4000 99664.3000
## 515 30614.2000 21120.6000
## 516 39093.0000 37839.2000
## 517 30180.2000 36781.8000
## 518 30227.6000 51226.2000
## 519 34715.6000 56710.5000
## 520 24055.8000 22787.1000
## 521 25337.5000 21935.1000
## 522 17405.7000 27928.5000
## 523 20633.0000 15164.1000
## 524 29138.9000 16159.4000
## 525 35487.1000 59137.7000
## 526 105893.0000 85255.8000
## 527 66378.2000 55225.1000
## 528 70270.9000 84526.2000
## 529 39741.3000 45294.3000
## 530 26878.6000 25317.1000
## 531 14025.0000 4012.9100
## 532 9201.0800 19838.9000
## 533 20833.8000 17039.4000
## 534 13929.8000 19043.0000
## 535 22986.8000 18402.2000
## 536 9118.3900 10617.4000
## 537 21434.5000 16966.3000
## 538 15977.5000 19967.2000
## 539 12686.5000 3376.1400
## 540 34865.5000 17960.3000
## 541 22349.4000 16122.3000
## 542 30470.2000 52723.6000
## 543 57348.2000 31485.3000
## 544 35487.1000 6644.2900
## 545 34905.6000 23619.3000
## 546 25413.2000 10734.6000
## 547 19111.7000 12706.6000
## 548 20166.6000 38438.7000
## 549 15277.2000 5111.7100
## 550 20668.9000 19963.0000
## 551 17648.9000 28514.9000
## 552 17222.0000 15927.5000
## 553 48136.2000 29330.6000
## 554 17694.6000 10970.0000
## 555 37178.6000 28876.8000
## 556 102234.0000 34469.0000
## 557 49951.1000 93115.8000
## 558 204477.0000 349546.0000
## 559 11920.6000 2076.3400
## 560 99005.5000 71260.0000
## 561 10139.2000 368.0430
## 562 48630.5000 41438.7000
## 563 20781.8000 29790.5000
## 564 35911.2000 40893.7000
## 565 32672.4000 31604.6000
## 566 42361.3000 68949.2000
## 567 25134.7000 55454.2000
## 568 23668.7000 20275.8000
## 569 29661.1000 24777.1000
## 570 22823.4000 23384.7000
## 571 30170.4000 18136.6000
## 572 4660.7300 42045.6000
## 573 34856.7000 52966.2000
## 574 30064.0000 45789.3000
## 575 23576.3000 50219.5000
## 576 22368.5000 32549.2000
## 577 33680.9000 52577.6000
## 578 52387.3000 51588.8000
## 579 42099.2000 27613.3000
## 580 24495.7000 20431.8000
## 581 30543.3000 27121.0000
## 582 26444.5000 30503.5000
## 583 37089.7000 28475.7000
## 584 26635.7000 27131.1000
## 585 15335.1000 15324.4000
## 586 32279.5000 35102.1000
## 587 86594.5000 30417.5000
## 588 3526.5600 15862.6000
## 589 36648.3000 40810.6000
## 590 40925.5000 51117.1000
## 591 17067.9000 36884.9000
## 592 30499.8000 28313.0000
## 593 85182.5000 30966.2000
## 594 35165.8000 40734.6000
## 595 27297.0000 28398.8000
## 596 29785.1000 58137.5000
## 597 21354.3000 36197.8000
## 598 31402.0000 29312.2000
## 599 49062.1000 46681.4000
## 600 18741.1000 30925.4000
## 601 41212.6000 21264.7000
## 602 202783.0000 33887.6000
## 603 50521.8000 44310.4000
## 604 63455.7000 67481.0000
## 605 37411.4000 40266.2000
## 606 49623.5000 32454.1000
## 607 108820.0000 49920.4000
## 608 38216.9000 28951.0000
## 609 49915.4000 16222.7000
## 610 38017.1000 4055.6600
## 611 29382.2000 38288.3000
## 612 24155.7000 34672.6000
## 613 43819.3000 52256.7000
## 614 33326.4000 51550.8000
## 615 30341.8000 29402.4000
## 616 32610.0000 44651.7000
## 617 25329.8000 56762.8000
## 618 25454.0000 26372.9000
## 619 40129.1000 19559.2000
## 620 9419.6200 38412.3000
## 621 20763.6000 11152.5000
## 622 54803.8000 52946.7000
## 623 42133.1000 43507.6000
## 624 32183.3000 49419.5000
## 625 28893.2000 20151.8000
## 626 24907.8000 10938.5000
## 627 16448.0000 18456.6000
## 628 17476.3000 30849.5000
## 629 35984.1000 6045.5300
## 630 30690.1000 24260.7000
## 631 24334.0000 26217.2000
## 632 36903.7000 100378.0000
## 633 32636.2000 45264.2000
## 634 31248.9000 22151.3000
## 635 2760.3200 54409.8000
## 636 27844.3000 31718.0000
## 637 25529.2000 100190.0000
## 638 33252.9000 15358.1000
## 639 39950.3000 31214.0000
## 640 20517.5000 39404.8000
## 641 192644.0000 1013.9200
## 642 37259.1000 4258.4500
## 643 20276.4000 24787.6000
## 644 27336.9000 24291.1000
## 645 89966.1000 63407.8000
## 646 37431.8000 46465.3000
## 647 15335.1000 13891.5000
## 648 12983.7000 11791.1000
## 649 42188.9000 28029.1000
## 650 8161.1900 8305.3600
## 651 21725.3000 21192.7000
## 652 15647.8000 14467.5000
## 653 36706.1000 26183.7000
## 654 54913.6000 60835.0000
## 655 27798.7000 47600.3000
## 656ddata4 <- decompose(tsdata4, "multiplicative")
plot(ddata4)
plot(ddata4$seasonal)
plot(ddata4$trend)
plot(allrace$avg_wage,type ="l",main = "Average salary for all races",xlab= "Year",ylab="Average Salary")
#class
class(tsdata4)## [1] "ts"mymodel4 <- auto.arima(tsdata4)
mymodel4## Series: tsdata4
## ARIMA(3,1,2)(1,0,1)[12]
##
## Coefficients:## Warning in sqrt(diag(x$var.coef)): NaNs produced## ar1 ar2 ar3 ma1 ma2 sar1 sma1
## 0.6770 0.0438 0.0287 -1.4511 0.4616 -0.2754 0.265
## s.e. 0.0871 0.0222 0.0220 0.0867 0.0839 NaN NaN
##
## sigma^2 estimated as 710058227: log likelihood=-91316.81
## AIC=182649.6 AICc=182649.6 BIC=182705.4auto.arima(tsdata4,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,1,2)(1,0,1)[12] with drift : 182708.3
## ARIMA(0,1,0) with drift : 186329
## ARIMA(1,1,0)(1,0,0)[12] with drift : 184340
## ARIMA(0,1,1)(0,0,1)[12] with drift : 182893.8
## ARIMA(0,1,0) : 186327
## ARIMA(2,1,2)(0,0,1)[12] with drift : 182635.2
## ARIMA(2,1,2) with drift : 182633.7
## ARIMA(2,1,2)(1,0,0)[12] with drift : 182709.5
## ARIMA(1,1,2) with drift : 182669
## ARIMA(2,1,1) with drift : 182716.4
## ARIMA(3,1,2) with drift : 182633.6
## ARIMA(3,1,2)(1,0,0)[12] with drift : 182616.1
## ARIMA(3,1,2)(2,0,0)[12] with drift : 182706.9
## ARIMA(3,1,2)(1,0,1)[12] with drift : 182614.6
## ARIMA(3,1,2)(0,0,1)[12] with drift : 182635.2
## ARIMA(3,1,2)(2,0,1)[12] with drift : 182698.3
## ARIMA(3,1,2)(1,0,2)[12] with drift : 182615.4
## ARIMA(3,1,2)(0,0,2)[12] with drift : 182636.3
## ARIMA(3,1,2)(2,0,2)[12] with drift : 182694.9
## ARIMA(3,1,1)(1,0,1)[12] with drift : 182641.3
## ARIMA(4,1,2)(1,0,1)[12] with drift : 182651.3
## ARIMA(3,1,3)(1,0,1)[12] with drift : 182617
## ARIMA(2,1,1)(1,0,1)[12] with drift : 182722.2
## ARIMA(2,1,3)(1,0,1)[12] with drift : 182720.1
## ARIMA(4,1,1)(1,0,1)[12] with drift : 182658.3
## ARIMA(4,1,3)(1,0,1)[12] with drift : 182647.6
## ARIMA(3,1,2)(1,0,1)[12] : 182613
## ARIMA(3,1,2)(0,0,1)[12] : 182633.2
## ARIMA(3,1,2)(1,0,0)[12] : 182614.3
## ARIMA(3,1,2)(2,0,1)[12] : 182696.4
## ARIMA(3,1,2)(1,0,2)[12] : 182613.5
## ARIMA(3,1,2) : 182631.8
## ARIMA(3,1,2)(0,0,2)[12] : 182634.4
## ARIMA(3,1,2)(2,0,0)[12] : 182705
## ARIMA(3,1,2)(2,0,2)[12] : 182693
## ARIMA(2,1,2)(1,0,1)[12] : 182706.3
## ARIMA(3,1,1)(1,0,1)[12] : 182638.7
## ARIMA(4,1,2)(1,0,1)[12] : 182648.9
## ARIMA(3,1,3)(1,0,1)[12] : 182615.2
## ARIMA(2,1,1)(1,0,1)[12] : 182719.4
## ARIMA(2,1,3)(1,0,1)[12] : 182716
## ARIMA(4,1,1)(1,0,1)[12] : 182656.3
## ARIMA(4,1,3)(1,0,1)[12] : 182645.6
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(3,1,2)(1,0,1)[12] : 182649.6
##
## Best model: ARIMA(3,1,2)(1,0,1)[12]## Series: tsdata4
## ARIMA(3,1,2)(1,0,1)[12]
##
## Coefficients:## Warning in sqrt(diag(x$var.coef)): NaNs produced## ar1 ar2 ar3 ma1 ma2 sar1 sma1
## 0.6770 0.0438 0.0287 -1.4511 0.4616 -0.2754 0.265
## s.e. 0.0871 0.0222 0.0220 0.0867 0.0839 NaN NaN
##
## sigma^2 estimated as 710058227: log likelihood=-91316.81
## AIC=182649.6 AICc=182649.6 BIC=182705.4residuals
library(tseries)
adf.test(mymodel4$residuals)## Warning in adf.test(mymodel4$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel4$residuals
## Dickey-Fuller = -18.516, Lag order = 19, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel4$residuals)
acf(ts(mymodel4$residuals),main = "ACF Residual")
pacf(ts(mymodel4$residuals),main = "PACF Residual")
#forecasting for all races and mean
myforecast4 <- forecast(mymodel4, level =c (95), h= 3*12)
plot(myforecast4,main = "Salary forecasting for all races in next 3years",xlab = "Time", ylab ="Average salary")
myforecast4[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 656
## 657 36959.67 37028.49 36784.33 35961.21 35761.24 36274.87 35969.23
## 658 35830.88 35742.86 35756.18 35940.83 35963.08 35795.99 35860.20
## 659 35843.35 35864.08 35857.67 35804.66 35796.86 35841.59 35822.88
## Aug Sep Oct Nov Dec
## 656 39905.47 38711.00 38260.37 37600.15 37053.08
## 657 35888.92 35844.97 35820.60 35857.96 35893.62
## 658 35866.72 35866.66 35863.88 35846.19 35830.58
## 659forecasting for race 1: White
library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
wrace <- subset(newrace, race_name == "White",select = c(avg_wage, year,race_name))
head(wrace)wrace$avg_wage <- as.numeric(wrace$avg_wage)
wrace$Date <- paste (wrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata5 <- ts(wrace$avg_wage,frequency = 12)
tsdata5## Jan Feb Mar Apr May Jun Jul
## 1 66809.00 32865.60 160712.00 41850.10 62279.30 80406.10 56667.70
## 2 78688.60 49086.00 113560.00 56078.70 26407.60 32023.50 30002.70
## 3 46748.30 107549.00 37840.80 91407.80 65354.10 18360.80 36797.40
## 4 22047.00 25169.60 24499.90 31737.80 30693.70 55755.30 36748.80
## 5 48242.50 70699.50 39622.50 24633.20 54995.00 28061.40 70642.10
## 6 28131.50 33888.80 96495.20 44548.70 44362.40 68532.30 133997.00
## 7 43785.60 46306.00 27067.70 14515.10 71908.60 85769.50 51115.80
## 8 82468.50 37260.70 75928.90 51015.20 76178.30 40049.30 64392.60
## 9 34267.70 17572.50 28573.90 62135.00 38133.70 27029.60 8531.65
## 10 65387.80 11067.70 33640.30 28867.20 22423.40 22786.70 27255.10
## 11 46485.30 66964.30 48036.90 43281.50 32898.60 11362.10 33480.00
## 12 37909.40 92336.10 49350.80 65838.00 35975.40 89997.20 46660.20
## 13 42004.50 72093.70 42509.40 24540.80 60550.00 46409.20 39981.00
## 14 43032.90 30932.60 31903.50 56124.50 31890.70 25713.90 81099.50
## 15 52192.20 50388.60 34759.40 89524.90 29931.70 55818.50 36743.90
## 16 74828.50 57865.10 49156.20 48391.30 39448.10 43373.60 18879.10
## 17 46854.10 27909.20 34830.10 58009.20 39492.00 23000.20 26310.10
## 18 16525.20 21583.50 23362.50 24884.30 34539.30 31536.70 24987.60
## 19 30612.50 32326.80 9194.38 18601.50 37078.90 75589.90 55129.10
## 20 79270.10 41719.80 29859.70 36103.80 63982.90 59275.90 25018.20
## 21 135886.00 41456.10 20299.30 17799.30 37489.20 16037.70 41882.00
## 22 28896.10 29595.80 39344.20 15797.50 25582.60 36729.30 35087.20
## 23 92657.70 71247.00 81844.10 26831.30 33462.20 82880.00 28389.60
## 24 82654.70 47596.40 48614.00 35987.10 17896.70 27038.00 41659.00
## 25 52831.70 32753.90 32986.90 48236.40 43215.90 81584.90 38754.30
## 26 47567.60 28429.00 54385.30 20228.50 32139.40 27318.20 25737.90
## 27 16697.80 28828.50 99752.80 63055.80 40924.80 60989.80 19799.00
## 28 27269.60 26455.10 69406.80 42966.10 102343.00 57891.30 46354.50
## 29 32109.00 44308.00 22429.30 16765.60 26456.30 35203.80 36567.10
## 30 18477.30 20276.80 117913.00 35982.90 156341.00 98904.90 21866.80
## 31 71110.70 201876.00 35318.40 25985.20 77033.70 58064.70 53742.70
## 32 25308.40 21273.10 20521.50 24994.80 28643.90 56434.50 44212.70
## 33 62577.00 76539.20 58013.70 52403.90 49603.00 74763.80 74119.00
## 34 24713.60 43698.80 27060.90 57877.50 28573.10 39209.60 11148.50
## 35 58148.60 107384.00 60076.90 70745.20 23091.70 45341.30 31718.60
## 36 71792.00 28485.30 30603.50 33100.50 66003.80 32816.50 42171.10
## 37 20221.80 33890.30 31637.80 42896.40 49165.00 37396.70 46833.40
## 38 18012.10 57922.20 43757.70 22076.90 24749.40 38741.40 23464.00
## 39 78528.70 55595.80 25202.90 64020.50 36493.00 53736.90 34379.00
## 40 17306.20 47928.30 54462.60 28607.90 35615.50 40731.30 24289.60
## 41 87190.10 82372.00 65316.30 97743.10 87542.10 71645.40 74336.10
## 42 70991.50 64696.30 116643.00 46351.00 77578.20 60725.60 79202.70
## 43 51855.40 53305.30 40436.10 49091.90 54131.80 63263.30 59618.70
## 44 53537.60 55073.10 62020.90 58509.00 65030.40 56484.00 82179.10
## 45 65207.90 52736.80 40575.30 80284.60 79485.10 75326.80 92066.60
## 46 83962.30 50995.00 59392.20 167746.00 69528.30 65790.40 74413.90
## 47 87910.70 70047.30 78441.20 69992.30 83822.70 97645.20 72033.50
## 48 63707.30 53471.10 70522.50 76879.40 85399.80 75939.20 67428.70
## 49 58797.50 36027.70 38894.80 44512.20 54203.40 42589.60 38393.20
## 50 33436.80 28216.70 53468.30 136615.00 48110.60 48271.90 54163.80
## 51 40099.20 42087.70 20003.60 17964.20 45681.40 46372.50 46647.20
## 52 30087.90 48232.30 61038.20 57704.40 44058.60 53424.30 46798.60
## 53 169385.00 45771.00 95850.70 97079.90 206409.00 73687.50 78370.00
## 54 57561.60 42139.70 98915.90 57097.50 136840.00 67918.80 44452.70
## 55 31931.80 36930.90 32720.50 41999.70 50772.00 53186.10 25559.00
## 56 26798.00 22005.30 21278.20 26687.00 26624.20 50972.30 73492.30
## 57 67464.60 57985.00 30169.10 33196.30 56446.20 26775.20 11295.60
## 58 13515.50 24829.50 14849.00 8958.79 17577.70 17985.70 11667.60
## 59 20988.10 35814.10 21815.50 55271.10 40820.10 28911.80 19807.50
## 60 25620.00 26022.40 23062.50 19213.10 23431.90 24747.90 13295.70
## 61 74548.70 14374.40 27729.20 35251.10 28664.80 56005.60 62197.70
## 62 56926.40 91032.70 22399.70 22524.10 48220.40 47287.70 25346.10
## 63 26606.40 37245.70 64418.30 23317.50 53185.10 46552.20 36565.30
## 64 27241.80 19267.30 41349.80 39994.10 39024.30 21827.80 41126.60
## 65 37577.20 54029.70 51799.90 52026.80 43472.20 27403.10 19826.40
## 66 36434.80 26460.00 27343.10 25893.40 27493.70 40020.90 35205.30
## 67 21625.60 35036.30 53054.10 42906.60 29998.50 30330.40 27412.60
## 68 29084.60 32612.90 26490.90 35776.10 26076.90 27013.70 34750.90
## 69 29704.30 29007.10 29859.50 38185.30 40237.80 37542.10 43658.50
## 70 47875.40 40525.90 34952.60 32106.10 34650.90 38740.80 44034.20
## 71 42752.10 31049.00 29008.80 47050.00 37673.10 33457.50 35055.10
## 72 36631.40 28237.60 37168.50 42181.30 16278.90 32617.00 57668.90
## 73 21600.10 27927.60 19531.10 23451.00 12210.90 31418.50 41272.90
## 74 30232.80 41330.50 48160.60 35178.50 29683.70 25724.80 28460.50
## 75 24988.80 48507.30 2002.53 35501.80 24242.10 22694.20 28882.90
## 76 46208.90 46630.20 46633.40 44677.40 34404.20 22995.80 32095.20
## 77 36985.80 24290.70 34244.80 25997.50 24932.80 37709.20 52554.10
## 78 44567.40 28315.40 23346.40 34831.30 27192.70 16209.40 59373.60
## 79 18769.80 63289.10 24362.30 29537.20 48168.40 40856.50 28373.50
## 80 26108.60 34241.60 40259.40 82479.90 64308.20 29872.60 47146.80
## 81 67720.30 99220.40 90936.80 69463.80 71889.40 50879.00 84534.20
## 82 118946.00 48276.10 80511.80 60484.10 79405.80 86230.00 62810.00
## 83 38855.90 49443.30 55330.10 62415.50 58148.40 58090.60 55386.40
## 84 65250.40 61015.00 64998.50 58129.90 73731.70 58268.80 83999.60
## 85 38115.80 78270.70 89202.20 74317.70 97839.10 76591.40 48257.30
## 86 61542.40 176727.00 68658.30 56195.20 75607.50 54179.40 92637.10
## 87 81895.20 72797.90 86179.90 89626.70 75548.00 121743.00 89242.50
## 88 66692.30 79967.20 92698.90 83509.40 69880.70 56074.30 69662.20
## 89 39235.10 44616.20 54850.70 44272.40 38608.70 41736.50 41878.50
## 90 52274.40 138830.00 47909.30 47039.50 55701.60 21740.40 41206.70
## 91 22536.00 18661.80 44132.20 41766.80 46289.20 38153.20 65853.80
## 92 61806.00 65214.00 42803.70 57942.50 48566.50 32956.10 31806.50
## 93 105152.00 95394.30 215572.00 82666.10 84965.80 61125.40 64830.40
## 94 96691.10 57607.90 132799.00 67845.70 43408.50 90838.70 45585.00
## 95 33360.30 42918.90 50346.90 56710.50 25711.60 44438.80 41542.70
## 96 20769.60 28212.10 27106.80 51189.90 76129.40 87554.50 49980.30
## 97 39741.30 34052.20 55917.40 26878.60 11470.70 39530.70 14456.70
## 98 15407.70 9118.39 18351.70 18756.90 11908.80 9048.19 12711.00
## 99 23008.50 57348.20 41054.30 31943.70 20473.50 39541.70 22755.80
## 100 23367.60 20166.60 23635.90 25053.90 13666.50 20668.90 24218.50
## 101 29616.50 33123.30 28876.80 57179.20 61687.70 93053.70 34808.60
## 102 20927.20 26608.50 50055.20 48630.50 24749.70 25021.20 43442.20
## 103 68949.20 23696.80 55454.20 55728.60 36433.50 50241.80 28297.10
## 104 42045.60 39668.30 41255.50 22211.20 37344.40 35488.70 28268.80
## 105 52024.50 52469.70 46129.50 27613.30 19903.70 29426.80 31686.30
## 106 27131.10 27399.40 30085.30 39372.60 35550.10 36616.00 39852.90
## 107 55053.80 40925.50 30818.30 29869.30 29090.70 28628.80 26673.90
## 108 27297.00 36375.40 32607.90 27856.20 36197.80 36246.00 31402.00
## 109 30925.40 41446.70 45204.10 43770.50 48119.80 54570.90 51309.00
## 110 37411.40 32274.20 35011.90 40164.60 44923.60 49920.40 39387.00
## 111 28730.00 50508.20 38338.50 32898.70 37706.20 52588.40 43415.10
## 112 37794.20 32610.00 17806.30 33210.10 56762.80 48231.90 23581.40
## 113 17581.10 23181.00 11152.50 32475.20 43712.50 24209.00 26400.80
## 114 49419.50 34914.30 29025.00 26517.30 29771.30 21323.30 20399.00
## 115 2027.83 30459.60 25937.00 24104.50 30690.10 20503.50 23319.00
## 116 50161.70 42623.70 33560.00 22151.30 29839.90 32382.90 42189.50
## 117 38907.10 17917.10 25761.90 39950.30 105603.00 19966.30 29792.40
## 118 23688.30 35257.50 27336.90 18258.40 75947.20 63407.80 26288.20
## 119 25266.50 29904.90 48701.90 42188.90 29269.90 20173.30 21882.40
## 120 36706.10 85214.00 64537.40 31282.40 47600.30
## Aug Sep Oct Nov Dec
## 1 93734.20 60046.80 31928.00 59888.00 40904.30
## 2 49787.40 54561.00 70270.60 117975.00 36106.00
## 3 31941.00 34415.10 43688.10 22532.80 41869.60
## 4 76397.50 32432.70 33352.90 28201.20 44039.60
## 5 30049.60 31726.60 30964.00 27202.70 14651.20
## 6 13353.50 83792.40 86123.80 54893.70 19110.10
## 7 64047.60 79293.70 95449.80 40411.70 74609.40
## 8 49667.70 35606.40 27489.20 71965.10 43264.10
## 9 35825.90 36989.10 49701.90 62771.50 47626.70
## 10 77953.60 28512.30 42574.40 160879.00 50598.40
## 11 21819.00 42512.90 39549.80 34424.10 23808.90
## 12 60868.50 24002.40 39348.10 31071.10 36073.80
## 13 53007.40 30507.00 71670.90 59479.70 24959.70
## 14 101924.00 83879.90 67800.90 65491.80 66318.80
## 15 36941.60 57238.80 73357.90 46606.40 42919.00
## 16 23377.10 12143.20 13076.20 31991.80 16775.70
## 17 86680.20 39539.70 91408.70 50802.10 30667.20
## 18 37508.40 42185.00 20727.80 17713.00 16651.80
## 19 44203.30 71697.00 74405.10 28182.70 34634.70
## 20 21352.40 28954.60 51127.00 20565.10 21803.70
## 21 35874.50 61987.90 64805.40 37620.50 55895.30
## 22 82116.00 87907.80 61853.50 85990.20 25030.40
## 23 33355.50 41366.70 36106.40 37322.50 27974.50
## 24 29327.90 88750.30 16129.30 35556.50 12004.30
## 25 70491.00 36528.70 30148.00 36019.60 19747.40
## 26 38835.20 18993.50 28778.30 47469.00 24051.10
## 27 143760.00 92251.60 26439.10 59632.10 45283.00
## 28 54065.10 71993.60 49168.80 64163.60 48320.20
## 29 48355.40 18527.30 35316.60 29809.20 38025.40
## 30 27557.30 28459.70 21950.60 37064.40 66559.70
## 31 52289.40 51470.10 54906.20 79085.00 46678.50
## 32 62278.80 60472.90 42893.40 32251.80 61466.00
## 33 50281.70 57578.20 33889.30 55148.90 21437.80
## 34 55857.80 28964.20 42704.40 29812.10 26558.10
## 35 84482.30 39987.30 26987.30 18235.70 32093.90
## 36 84351.80 41625.20 57276.70 45464.80 37431.80
## 37 25654.90 23057.50 54200.70 39800.70 27393.40
## 38 39772.60 42783.20 57049.60 52648.00 22117.00
## 39 25055.60 72433.30 11799.50 104688.00 22352.50
## 40 28050.20 46371.60 88959.20 139449.00 78877.00
## 41 50999.70 83197.70 77236.30 71461.10 57161.20
## 42 89019.20 62049.30 63468.50 62573.60 81803.20
## 43 56102.80 53894.70 83352.50 38396.80 53295.10
## 44 59798.60 79446.30 119806.00 58866.50 91589.20
## 45 75717.20 49367.00 82365.40 78330.90 62805.10
## 46 49647.00 93336.80 95417.00 86324.00 72590.30
## 47 104300.00 87753.60 42155.50 52775.80 38001.90
## 48 60460.00 64071.40 100991.00 62686.50 72966.50
## 49 41898.70 42388.50 33288.60 41684.60 42898.70
## 50 21478.60 41432.90 44805.60 40482.70 32834.30
## 51 33398.40 64846.90 38249.40 23948.40 29812.10
## 52 34614.80 28909.90 39538.40 44002.60 80154.30
## 53 59327.70 64958.40 68225.00 31915.50 56719.50
## 54 92084.00 45668.50 42972.50 47494.30 45394.40
## 55 41504.50 40184.80 24789.50 29532.40 27057.90
## 56 86242.90 49834.80 65591.80 68112.50 41711.30
## 57 39811.60 15143.20 34195.40 29704.60 17406.80
## 58 9200.38 11976.30 36852.80 38324.10 15915.40
## 59 38590.20 25386.40 10924.80 15902.10 49710.60
## 60 19943.10 25580.50 11043.10 18433.10 47869.90
## 61 85487.70 35093.10 62421.90 71307.30 18917.90
## 62 23830.00 48587.70 32125.10 30509.60 33075.20
## 63 50303.80 28421.10 43817.00 25555.10 19188.20
## 64 35628.40 28496.00 29596.80 34233.00 33457.70
## 65 27625.10 31413.30 36019.90 26603.50 28814.50
## 66 35504.50 42005.00 16482.30 18081.10 38446.30
## 67 28149.40 26910.40 35947.70 23373.30 38402.20
## 68 34098.50 29335.30 21720.00 47937.20 70492.00
## 69 43574.10 47157.00 31181.70 56457.40 40229.30
## 70 47189.50 37374.30 34373.00 34486.60 41394.30
## 71 48215.80 43445.00 61372.40 42202.20 48395.50
## 72 44456.70 23644.50 46231.70 33445.80 26381.30
## 73 24083.90 34922.00 28168.90 30143.00 38542.60
## 74 21328.40 21713.10 18924.60 18729.20 32323.20
## 75 21469.30 25358.30 23481.90 25838.20 84919.80
## 76 35636.00 42136.10 30612.80 33156.00 24687.40
## 77 18201.20 28800.80 49886.90 115885.00 78951.50
## 78 56608.20 25512.30 67635.90 57562.90 17940.50
## 79 19094.40 21248.80 24228.40 18107.90 43924.70
## 80 88936.80 145273.00 73620.70 88034.70 82490.50
## 81 77419.40 68923.00 60085.70 71770.90 64575.00
## 82 61739.00 54679.80 82712.90 52754.80 59448.10
## 83 84512.90 40204.00 55241.20 54526.60 56204.90
## 84 123048.00 60200.20 105987.00 69577.50 50193.10
## 85 82429.00 78522.60 66090.70 82730.70 51851.70
## 86 91187.80 90102.60 78198.40 90141.40 64047.80
## 87 42578.50 51655.60 40775.90 65290.60 50779.50
## 88 100238.00 63901.00 72110.60 55461.00 39702.50
## 89 35904.40 44000.90 43635.10 34340.50 27663.50
## 90 45025.60 40143.00 33348.90 41599.00 41984.20
## 91 37927.70 25551.50 32701.30 28396.10 56801.10
## 92 37848.10 43949.40 78947.50 172133.00 47651.70
## 93 72824.20 34504.20 56704.10 55170.00 43559.20
## 94 45592.90 49161.00 45811.30 31955.90 36781.80
## 95 25865.40 29865.60 27928.50 28233.60 22992.50
## 96 67737.20 66478.60 42327.70 69474.80 59275.10
## 97 33804.10 29665.10 17892.40 13352.30 26721.20
## 98 38084.70 38779.20 16237.10 21399.00 36532.40
## 99 6806.93 16004.20 52447.80 26469.80 25201.90
## 100 10325.00 18637.00 48013.70 76344.20 14801.50
## 101 65171.90 73683.60 19700.20 59894.20 99005.50
## 102 34201.90 31346.20 33833.60 24979.80 37983.10
## 103 45113.30 24610.80 19011.10 28739.20 19865.50
## 104 32324.50 33054.00 34632.60 38992.00 54196.50
## 105 37958.30 26444.50 29688.00 37089.70 27691.00
## 106 17722.30 18198.40 37053.60 24325.50 36648.30
## 107 37519.20 23001.10 38852.30 27867.80 30275.60
## 108 21986.20 50171.70 68392.10 27365.70 32856.00
## 109 33887.60 57466.30 40355.80 48837.80 40360.40
## 110 38216.90 34921.10 42477.10 44480.90 32207.70
## 111 65206.20 43555.40 51550.80 37423.50 29995.00
## 112 49261.90 33340.20 26835.10 21569.50 29069.80
## 113 23393.60 31757.40 42133.10 32607.00 39458.50
## 114 18456.60 19236.60 30849.50 24745.30 40344.40
## 115 26217.20 27173.80 86894.60 47099.90 47106.20
## 116 33881.80 34123.50 26161.20 38154.20 29648.00
## 117 48582.70 120657.00 79194.30 47034.20 62709.30
## 118 67113.60 54090.90 16442.50 18962.40 67934.90
## 119 22885.60 17892.30 44343.60 25443.80 31635.40
## 120ddata5 <- decompose(tsdata5, "multiplicative")
plot(ddata5)
plot(ddata5$seasonal)
plot(ddata5$trend)
plot(wrace$avg_wage,type ="l",main = "Average salary for all whites",xlab= "Year",ylab="Average Salary")
class(tsdata5)## [1] "ts"mymodel5 <- auto.arima(tsdata5)
mymodel5## Series: tsdata5
## ARIMA(2,1,3)(2,0,2)[12]
##
## Coefficients:## Warning in sqrt(diag(x$var.coef)): NaNs produced## ar1 ar2 ma1 ma2 ma3 sar1 sar2 sma1
## 0.4654 0.3083 -1.2364 0.0203 0.2287 -0.6974 -0.3526 0.7364
## s.e. NaN NaN NaN NaN NaN 0.8966 1.0787 0.8606
## sma2
## 0.4296
## s.e. 1.0595
##
## sigma^2 estimated as 485528596: log likelihood=-16348.89
## AIC=32717.78 AICc=32717.93 BIC=32770.44auto.arima(tsdata5,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,1,2)(1,0,1)[12] with drift : 32705.05
## ARIMA(0,1,0) with drift : 33370.61
## ARIMA(1,1,0)(1,0,0)[12] with drift : 32995.47
## ARIMA(0,1,1)(0,0,1)[12] with drift : 32726.98
## ARIMA(0,1,0) : 33368.61
## ARIMA(2,1,2)(0,0,1)[12] with drift : 32719.19
## ARIMA(2,1,2)(1,0,0)[12] with drift : 32705.16
## ARIMA(2,1,2)(2,0,1)[12] with drift : 32663.92
## ARIMA(2,1,2)(2,0,0)[12] with drift : 32674.99
## ARIMA(2,1,2)(2,0,2)[12] with drift : 32650.25
## ARIMA(2,1,2)(1,0,2)[12] with drift : 32706.37
## ARIMA(1,1,2)(2,0,2)[12] with drift : 32692.56
## ARIMA(2,1,1)(2,0,2)[12] with drift : 32669.88
## ARIMA(3,1,2)(2,0,2)[12] with drift : 32678.32
## ARIMA(2,1,3)(2,0,2)[12] with drift : 32652.27
## ARIMA(1,1,1)(2,0,2)[12] with drift : 32693.18
## ARIMA(1,1,3)(2,0,2)[12] with drift : 32689.74
## ARIMA(3,1,1)(2,0,2)[12] with drift : 32662.96
## ARIMA(3,1,3)(2,0,2)[12] with drift : 32656.5
## ARIMA(2,1,2)(2,0,2)[12] : 32648.25
## ARIMA(2,1,2)(1,0,2)[12] : 32704.41
## ARIMA(2,1,2)(2,0,1)[12] : 32661.93
## ARIMA(2,1,2)(1,0,1)[12] : 32703.09
## ARIMA(1,1,2)(2,0,2)[12] : 32690.6
## ARIMA(2,1,1)(2,0,2)[12] : 32667.88
## ARIMA(3,1,2)(2,0,2)[12] : 32676.36
## ARIMA(2,1,3)(2,0,2)[12] : 32650.44
## ARIMA(1,1,1)(2,0,2)[12] : 32691.25
## ARIMA(1,1,3)(2,0,2)[12] : 32687.78
## ARIMA(3,1,1)(2,0,2)[12] : 32661.03
## ARIMA(3,1,3)(2,0,2)[12] : 32654.51
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(2,1,2)(2,0,2)[12] : Inf
## ARIMA(2,1,2)(2,0,2)[12] with drift : Inf
## ARIMA(2,1,3)(2,0,2)[12] : 32717.78
##
## Best model: ARIMA(2,1,3)(2,0,2)[12]## Series: tsdata5
## ARIMA(2,1,3)(2,0,2)[12]
##
## Coefficients:## Warning in sqrt(diag(x$var.coef)): NaNs produced## ar1 ar2 ma1 ma2 ma3 sar1 sar2 sma1
## 0.4654 0.3083 -1.2364 0.0203 0.2287 -0.6974 -0.3526 0.7364
## s.e. NaN NaN NaN NaN NaN 0.8966 1.0787 0.8606
## sma2
## 0.4296
## s.e. 1.0595
##
## sigma^2 estimated as 485528596: log likelihood=-16348.89
## AIC=32717.78 AICc=32717.93 BIC=32770.44library(tseries)
adf.test(mymodel5$residuals)## Warning in adf.test(mymodel5$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel5$residuals
## Dickey-Fuller = -10.855, Lag order = 11, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel5$residuals)
acf(ts(mymodel5$residuals),main = "ACF Residual")
pacf(ts(mymodel5$residuals),main = "PACF Residual")
myforecast5 <- forecast(mymodel5, level =c (95), h= 3*12)
plot(myforecast5,main = "Salary forecasting for all whites in next 3years",xlab = "Time", ylab ="Average salary")
myforecast5[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 120 45781.88 42961.61
## 121 41862.71 42282.04 42702.85 41518.70 39489.24 38157.52 40283.93
## 122 40113.54 42470.95 40530.14 39275.68 42229.34 41467.26 40565.86
## 123 39725.54 37819.71 38929.63 40142.53 38731.98
## Aug Sep Oct Nov Dec
## 120 43212.90 38161.66 39385.30 40030.80 41188.51
## 121 38293.63 39771.72 41494.42 40452.27 38472.56
## 122 41531.75 41999.46 40132.35 40435.36 41244.23
## 123forecasting for race 2: Black or African American
library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
brace <- subset(newrace, race_name == "Black or African American",select = c(avg_wage, year,race_name))
head(brace)brace$avg_wage <- as.numeric(brace$avg_wage)
brace$Date <- paste (brace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata6 <- ts(brace$avg_wage,frequency = 12)
tsdata6## Jan Feb Mar Apr May Jun Jul
## 1 48178.60 29250.20 50330.30 53659.00 47527.00 32677.90 70695.00
## 2 45883.90 97502.70 32346.40 42188.70 26625.00 5689.51 15140.30
## 3 73606.30 93783.50 23891.80 28856.00 28124.70 31060.40 25406.30
## 4 33334.30 29707.00 46985.70 31204.60 51243.70 31534.90 32892.90
## 5 43800.50 25926.20 28036.20 25401.50 27817.20 19199.20 12937.70
## 6 55165.20 96550.00 17694.00 33228.70 68800.60 43300.70 20375.10
## 7 53026.40 42219.40 52504.20 180532.00 79147.70 37210.90 82190.30
## 8 38313.70 45320.90 35091.70 23558.90 55211.30 35220.40 25587.80
## 9 8736.67 34381.40 29952.30 43100.80 70533.20 38084.60 51838.60
## 10 27451.60 54174.60 25619.00 46391.70 114335.00 71564.10 34108.30
## 11 30252.50 19338.20 33032.20 30493.80 34236.10 22675.70 30635.80
## 12 123330.00 39143.30 28782.70 36712.90 56895.10 22692.50 41209.00
## 13 50559.80 24895.90 56102.00 52710.70 32167.60 42030.50 36809.70
## 14 55608.10 59788.30 59113.10 67329.70 42511.80 58046.20 40995.30
## 15 50711.80 47684.30 40054.60 29858.70 58405.10 45971.00 43662.50
## 16 33332.90 14265.40 30757.00 16251.60 36720.70 8911.58 47665.10
## 17 69852.50 27439.60 19379.80 19896.30 19400.80 19683.40 18693.20
## 18 27637.00 17050.10 28710.60 36261.40 12697.90 19983.50 24434.00
## 19 31553.90 36615.50 57878.40 27553.90 23247.10 30434.40 46366.50
## 20 21327.60 18217.80 86012.10 40677.70 15849.20 19920.70 37774.70
## 21 38184.00 43938.50 16409.90 30558.70 41882.20 17353.50 22923.70
## 22 66493.80 27449.40 71682.60 76462.60 57964.50 26502.00 31007.10
## 23 26645.80 27839.10 73144.60 39799.40 14239.50 33252.10 19446.30
## 24 63709.80 18033.00 38130.50 32093.20 27530.00 40547.90 38591.50
## 25 32821.30 20305.00 32112.40 32400.50 44985.80 1354.15 41181.00
## 26 24523.00 12158.90 14802.00 36490.40 56419.50 55688.40 39557.80
## 27 54582.70 65547.60 27321.50 22492.50 56560.80 35313.70 25724.70
## 28 55210.20 28235.20 29060.80 44306.50 27835.60 17799.80 29266.50
## 29 24028.70 36150.80 12708.40 19944.00 53893.10 25329.60 109414.00
## 30 35764.10 55792.60 50378.60 181223.00 36935.20 24603.50 54664.90
## 31 78321.60 33705.60 24501.50 20894.80 16525.30 10240.40 32072.00
## 32 44600.30 39890.40 51990.30 38928.70 47134.70 12243.90 45907.20
## 33 22462.20 22445.60 22939.10 24920.20 57078.20 30147.00 29473.50
## 34 26709.40 30789.10 77124.80 37789.20 47029.40 51553.70 43963.20
## 35 35541.80 38060.00 26807.90 16412.00 32852.90 43062.30 26821.30
## 36 17223.80 21873.50 29797.30 27422.50 20611.60 44038.00 29333.70
## 37 22854.50 22316.40 32739.90 36550.40 16474.50 26133.90 26698.80
## 38 46476.90 24783.90 38549.80 29186.90 45826.80 33060.90 21827.00
## 39 42124.20 43070.40 30261.40 32499.80 24946.70 31008.70 25673.90
## 40 42303.30 53455.40 76071.60 56782.40 55594.90 75390.70 46117.20
## 41 55928.40 58157.10 36517.50 27087.10 35647.00 74811.00 56327.90
## 42 24246.70 45245.30 42315.60 51835.50 49173.70 46020.20 43953.90
## 43 38635.10 41774.00 47484.00 62940.50 60104.60 70912.50 55377.90
## 44 20712.10 45403.50 61066.10 59080.40 67396.50 39802.20 71624.80
## 45 52111.90 26030.10 73783.10 27789.70 64953.20 78166.60 75435.40
## 46 55780.70 76096.50 62842.10 31598.80 50645.60 26378.00 18916.80
## 47 57971.40 52571.10 49732.40 59643.70 72224.00 36004.60 39043.40
## 48 38779.20 37643.30 27751.20 26217.90 41765.40 31170.90 44998.60
## 49 21512.10 40433.40 43780.70 40064.60 29208.20 32280.60 36700.10
## 50 49210.30 36252.70 70139.50 12255.10 40358.40 27993.50 24236.70
## 51 21008.80 26157.40 24338.60 35213.40 32282.80 117159.00 30787.00
## 52 48064.80 54390.40 57847.90 17333.60 65322.50 50739.90 43182.10
## 53 38471.20 43039.70 47882.10 44937.20 30718.30 36106.90 32592.30
## 54 39716.10 26782.60 24255.00 27051.30 26651.50 35422.80 30444.70
## 55 40061.30 56245.80 58848.10 41369.10 72552.20 59507.00 27561.70
## 56 19361.80 24973.80 20932.40 17276.20 14538.40 24716.10 12615.90
## 57 16750.20 33170.20 31452.50 17567.70 19656.50 25882.00 19360.00
## 58 7338.75 17048.80 30961.50 11584.60 19719.70 19134.50 40267.60
## 59 15957.70 15622.20 37168.50 49566.50 12975.60 17861.40 22383.30
## 60 37035.70 44195.60 10447.80 32279.20 77213.50 17346.10 24193.70
## 61 33495.80 37095.00 32156.90 14301.50 35371.60 47648.40 25624.70
## 62 32025.60 26495.30 21689.40 27693.40 14495.30 45694.90 41967.70
## 63 22088.60 36983.40 31233.20 28043.20 49196.80 45698.90 44060.20
## 64 35012.00 30376.30 24813.80 28788.90 20653.30 27322.30 19560.30
## 65 16836.30 22462.70 43281.50 7631.27 40221.30 39077.20 51283.40
## 66 36868.20 26438.30 36542.20 20978.20 29716.30 27194.40 34332.80
## 67 33402.50 40248.60 63903.70 23161.80 23522.30 28031.10 42571.30
## 68 35674.60 42873.00 16690.20 33565.30 39049.30 24422.20 37368.30
## 69 40117.70 42656.70 28970.70 19341.90 39599.30 34317.90 40902.40
## 70 38069.50 30275.80 33110.50 15262.80 47885.70 27710.80 32577.70
## 71 22399.30 21964.20 23816.00 31168.00 24703.90 29949.90 29298.80
## 72 23111.30 33229.10 29100.60 24447.40 29439.60 10252.20 18284.90
## 73 10299.50 26551.70 13065.40 41171.20 30855.30 27583.00 87181.30
## 74 21577.20 28298.20 24255.50 27237.00 19647.30 25090.50 27930.40
## 75 31262.90 26827.30 14766.10 30721.10 45138.10 98093.90 64669.10
## 76 14849.10 63547.00 57637.60 33946.10 30643.80 37529.90 21067.30
## 77 26544.80 31612.80 19411.20 21762.00 14780.40 16832.80 28451.30
## 78 29525.00 40027.50 60078.40 100552.00 46153.10 62918.20 45233.70
## 79 46898.30 49897.90 60258.70 71032.20 47595.70 52895.90 53795.60
## 80 42938.40 55623.90 44369.40 40556.60 62527.10 38218.90 78994.90
## 81 47882.00 43429.30 54644.90 27144.10 48698.30 39618.80 43952.70
## 82 69565.60 52476.20 51790.20 43700.80 48606.40 51817.90 50828.30
## 83 35175.30 73146.00 47168.90 60104.30 92152.40 43921.50 48484.30
## 84 72085.70 80301.60 73380.80 69010.60 94880.60 54289.50 60594.50
## 85 53713.90 25542.90 19499.70 85866.80 47049.50 90137.90 95077.80
## 86 66293.70 34654.70 38448.50 39716.10 57214.40 37957.60 35565.80
## 87 36133.30 48649.40 81248.20 39470.90 38456.20 47151.00 21201.70
## 88 39957.90 24640.90 23716.70 30226.90 13113.40 32289.30 26359.20
## 89 28997.60 25439.60 47081.70 32556.00 40916.20 51389.40 20195.60
## 90 31412.90 120911.00 94890.20 167285.00 52268.80 49793.00 52680.10
## 91 72591.80 57915.60 151276.00 71590.90 25347.90 89646.40 39253.90
## 92 34104.00 34365.70 35358.00 41501.60 23300.70 39670.50 45183.90
## 93 31299.30 29138.90 23963.90 60830.10 68992.90 74923.80 43207.80
## 94 45294.30 30142.70 39900.20 25317.10 10255.40 39931.10 19875.20
## 95 12623.90 10617.40 15899.70 16144.30 13140.00 12884.20 17349.60
## 96 21613.70 31485.30 28669.00 23565.00 33243.10 12342.40 7209.14
## 97 38438.70 22485.10 15277.20 18300.30 19963.00 21273.80 15909.80
## 98 24701.20 18604.60 41897.80 43064.10 40589.90 33991.20 40831.00
## 99 28258.20 30050.30 41438.70 26068.60 20781.80 42565.00 33132.80
## 100 25705.80 35909.10 34819.60 34011.10 29442.10 23975.60 31020.50
## 101 45623.60 26509.00 21625.70 30064.00 30309.20 23816.20 22691.70
## 102 44137.80 40213.60 25002.80 19546.20 28885.50 30543.30 28736.10
## 103 20679.60 29293.50 24162.10 30655.10 34862.20 24091.60 17850.20
## 104 51117.10 28678.60 17067.90 22858.80 23608.10 24496.30 37790.10
## 105 35585.50 24738.90 26357.00 31204.20 36218.90 29312.20 15567.30
## 106 41212.60 33150.30 39400.80 86899.10 17309.40 52390.80 35575.50
## 107 39970.00 61936.40 38881.90 35828.50 28951.00 28785.60 38830.60
## 108 34400.50 41170.90 13296.50 43570.80 44728.90 38859.20 33370.90
## 109 33020.30 39530.70 29241.00 20432.20 21576.00 23495.80 21491.40
## 110 33570.10 401780.00 30048.90 43507.60 11280.20 33655.10 28893.20
## 111 29664.30 17519.90 15852.10 35984.10 18136.60 9611.06 24260.70
## 112 36903.70 44210.30 32636.20 42957.30 23672.10 20617.90 24825.20
## 113 35214.70 21913.60 44510.90 36561.00 3169.26 31214.00 19365.90
## 114 25400.90 27880.50 35538.60 24291.10 18292.40 67909.60 60690.10
## 115 49617.90 21448.60 38793.10 45365.30 28029.10 32712.40 19603.60
## 116 20059.70 26183.70 74940.60 69915.10 32174.10 43269.80
## Aug Sep Oct Nov Dec
## 1 35619.80 39364.50 19365.30 21903.90 55689.00
## 2 58975.50 56137.90 56568.70 33743.10 27099.00
## 3 17673.20 41439.90 22133.80 19753.00 23667.20
## 4 34497.90 32813.80 63754.90 38756.20 28572.50
## 5 25315.30 24652.10 93748.90 41573.50 46312.00
## 6 39038.70 39021.70 33799.60 13430.20 64758.20
## 7 88718.00 34209.10 67288.00 41730.30 59585.70
## 8 17727.40 26327.00 41609.20 34606.50 22088.10
## 9 8590.17 26071.10 24314.00 21704.30 17518.30
## 10 70032.40 43378.10 29488.40 35350.10 12644.00
## 11 49356.00 33419.40 58053.30 36657.50 85162.70
## 12 56603.60 32595.00 10704.50 43765.40 46238.00
## 13 42865.30 22817.10 25874.50 56939.40 46365.60
## 14 104821.00 21967.10 27536.60 33451.60 24464.00
## 15 38099.00 57346.10 37370.10 51071.50 26310.60
## 16 30631.20 11561.40 14390.10 24148.20 29551.10
## 17 30035.10 22484.70 36848.00 40284.50 35544.50
## 18 58806.60 46422.60 38047.10 68427.70 66713.00
## 19 51393.60 27525.30 19382.90 24147.20 53593.10
## 20 18677.80 41605.30 29264.50 66225.00 35609.20
## 21 31053.60 52430.50 49846.10 126505.00 37652.40
## 22 47865.60 35867.50 33987.30 28198.90 21330.00
## 23 24899.10 26309.90 14876.70 49235.90 17984.90
## 24 60614.20 39343.20 41687.00 22926.20 34183.50
## 25 21644.30 27851.20 26002.20 9371.39 12458.20
## 26 30613.10 18347.30 148458.00 83642.70 23749.80
## 27 48055.20 31543.60 34856.80 48633.30 45239.30
## 28 25888.30 25115.60 40803.80 19192.70 20759.30
## 29 99467.30 42548.90 29018.60 21617.30 19433.70
## 30 59317.70 49451.90 49373.00 44730.10 45792.70
## 31 42809.20 38224.40 46326.60 41538.80 38927.80
## 32 70374.30 56130.90 40463.70 60100.20 27305.70
## 33 15386.60 65781.80 19181.20 41546.30 396311.00
## 34 30673.00 39270.10 30912.50 25461.60 29165.70
## 35 39562.40 73941.90 42055.40 50042.50 45148.80
## 36 29301.90 25461.30 20243.10 53187.10 18723.30
## 37 26325.20 35757.10 26796.50 13522.40 67347.80
## 38 68374.80 5931.72 80115.80 30399.10 15936.50
## 39 59260.80 64453.70 96560.60 45930.40 58742.00
## 40 42591.30 61154.30 66839.20 40336.30 53058.70
## 41 46420.90 41505.80 40050.60 62371.90 36239.20
## 42 51375.30 22401.90 48099.10 40700.50 43302.30
## 43 56122.90 50479.20 44250.00 48958.50 55997.90
## 44 47408.30 56255.80 85293.10 41505.00 49222.30
## 45 82768.70 86915.00 61162.50 64792.00 133678.00
## 46 49944.80 87110.00 47999.90 93891.40 55256.10
## 47 28613.00 57118.60 28168.70 37832.40 34843.50
## 48 53654.70 81991.00 41161.70 38153.30 46309.60
## 49 20862.70 22870.00 32427.60 16796.30 32374.20
## 50 25095.40 57856.20 31308.00 38410.60 56243.70
## 51 25754.30 96484.50 186829.00 46517.10 54727.80
## 52 69256.10 56217.20 149710.00 25031.60 91271.40
## 53 34928.00 39162.60 40545.00 23545.00 39204.70
## 54 27603.70 24010.10 56763.40 60633.70 86161.90
## 55 26655.60 44734.80 24983.60 7240.76 31689.90
## 56 8863.53 15744.00 15912.60 12850.50 13034.60
## 57 23269.50 27626.30 24812.80 28791.50 12167.70
## 58 21409.40 13931.70 17689.20 20035.30 20863.30
## 59 18824.60 35699.70 42315.90 41839.80 35099.60
## 60 29384.60 39930.60 24347.70 26585.20 42033.80
## 61 33890.60 32808.70 31035.90 35440.10 24301.30
## 62 23998.60 22099.60 28890.70 30501.30 24884.80
## 63 35777.00 25466.30 20575.10 31816.20 31085.00
## 64 29176.40 23039.80 30399.10 35329.90 23222.10
## 65 28222.30 18261.70 24494.40 23606.80 25054.50
## 66 25274.90 25302.90 30275.10 33999.10 34609.20
## 67 31288.20 39719.60 85814.70 17581.80 50068.90
## 68 73263.70 43475.50 31566.90 28518.90 30464.30
## 69 40993.30 13306.00 41386.70 42693.90 34968.70
## 70 42291.60 32277.90 29373.80 6207.84 21645.40
## 71 33055.70 396767.00 29844.70 41623.90 21354.60
## 72 17905.40 29482.60 20052.40 35535.00 5365.47
## 73 34380.50 43540.60 33574.40 42154.70 23129.80
## 74 29108.40 23896.30 43955.50 32441.50 11869.70
## 75 37602.40 27604.30 27774.40 34160.80 24373.20
## 76 21377.40 44259.40 21849.80 42108.80 43399.20
## 77 26247.30 14600.10 36703.00 74548.80 67047.80
## 78 49696.90 70866.10 57140.90 60138.00 80271.00
## 79 61885.00 34476.40 26817.50 35841.70 72957.60
## 80 22341.30 46295.10 42855.30 49476.30 61684.80
## 81 35265.00 44388.80 48307.10 62758.70 58280.40
## 82 22842.80 55018.40 59726.90 61894.60 69914.70
## 83 52394.60 56472.50 37080.10 87423.60 28098.10
## 84 130497.00 56048.10 74669.60 80210.90 31139.20
## 85 59040.00 64378.10 43555.30 51176.10 60191.90
## 86 40361.80 37904.20 28596.40 28855.90 40602.30
## 87 40415.00 43551.70 40739.30 27879.70 32527.80
## 88 38876.30 85035.80 30147.80 36151.70 28152.10
## 89 24799.00 31718.60 39862.10 31770.80 132263.00
## 90 81779.10 17186.20 66933.60 48979.00 38106.00
## 91 30614.20 49469.70 39093.00 33647.90 35302.10
## 92 25337.50 26134.40 27252.40 26576.00 35324.60
## 93 56459.20 58238.40 41088.00 70270.90 58228.20
## 94 26847.00 20833.80 18316.30 15790.50 22986.80
## 95 29814.50 34865.50 18364.30 20684.40 30470.20
## 96 14604.20 36419.00 12207.80 19111.70 20954.40
## 97 15507.60 37422.90 48136.20 13340.40 22732.40
## 98 39014.40 11920.60 31489.50 71260.00 17671.10
## 99 38313.10 32672.40 11836.40 36917.90 46879.20
## 100 27263.70 21877.00 30170.40 19141.00 44978.80
## 101 34135.50 29892.30 33680.90 48474.00 48634.10
## 102 30503.50 24514.40 28475.70 19762.40 28578.90
## 103 23593.30 43311.40 7690.80 40810.60 42650.70
## 104 26229.40 36431.20 28936.80 22639.20 28398.80
## 105 29767.40 80665.40 23280.80 26090.60 31855.70
## 106 40893.00 22447.90 40266.20 39542.70 24013.50
## 107 39426.90 29303.20 20292.70 29382.20 37003.80
## 108 32461.70 46539.60 25153.70 44651.70 25557.70
## 109 25126.50 22577.90 25521.20 20859.00 30799.50
## 110 26366.90 29543.80 13101.20 20846.30 19981.80
## 111 12887.50 26361.80 30346.70 13298.50 100817.00
## 112 2760.32 27799.70 21366.80 27177.70 29399.70
## 113 32931.70 46372.20 93926.30 63516.20 37259.10
## 114 22902.50 31827.50 37431.80 27647.80 19838.70
## 115 21716.10 9525.84 16569.70 30916.90 27563.40
## 116ddata6 <- decompose(tsdata6, "multiplicative")
plot(ddata6)
plot(ddata6$seasonal)
plot(ddata6$trend)
plot(brace$avg_wage,type ="l",main = "Average salary for Blacks or African Americans",xlab= "Year",ylab="Average Salary")
class(tsdata6)## [1] "ts"mymodel6 <- auto.arima(tsdata6)
mymodel6## Series: tsdata6
## ARIMA(1,0,1) with non-zero mean
##
## Coefficients:
## ar1 ma1 mean
## 0.9006 -0.7813 39310.632
## s.e. 0.0363 0.0542 1520.541
##
## sigma^2 estimated as 6.69e+08: log likelihood=-16047.92
## AIC=32103.84 AICc=32103.87 BIC=32124.78auto.arima(tsdata6,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 32118.77
## ARIMA(0,0,0) with non-zero mean : 32200.79
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 32162.03
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 32161.66
## ARIMA(0,0,0) with zero mean : 33790.82
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 32110.21
## ARIMA(2,0,2) with non-zero mean : 32109.15
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 32116.9
## ARIMA(1,0,2) with non-zero mean : 32106.36
## ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 32115.12
## ARIMA(1,0,2)(0,0,1)[12] with non-zero mean : 32107.54
## ARIMA(1,0,2)(1,0,1)[12] with non-zero mean : 32116.34
## ARIMA(0,0,2) with non-zero mean : 32151.97
## ARIMA(1,0,1) with non-zero mean : 32104.56
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 32113.13
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 32105.56
## ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 32114.39
## ARIMA(0,0,1) with non-zero mean : 32166.13
## ARIMA(1,0,0) with non-zero mean : 32158.05
## ARIMA(2,0,1) with non-zero mean : 32108.27
## ARIMA(2,0,0) with non-zero mean : 32138.23
## ARIMA(1,0,1) with zero mean : Inf
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,0,1) with non-zero mean : 32103.84
##
## Best model: ARIMA(1,0,1) with non-zero mean## Series: tsdata6
## ARIMA(1,0,1) with non-zero mean
##
## Coefficients:
## ar1 ma1 mean
## 0.9006 -0.7813 39310.632
## s.e. 0.0363 0.0542 1520.541
##
## sigma^2 estimated as 6.69e+08: log likelihood=-16047.92
## AIC=32103.84 AICc=32103.87 BIC=32124.78library(tseries)
adf.test(mymodel6$residuals)## Warning in adf.test(mymodel6$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel6$residuals
## Dickey-Fuller = -11.157, Lag order = 11, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel6$residuals)
acf(ts(mymodel6$residuals),main = "ACF Residual")
pacf(ts(mymodel6$residuals),main = "PACF Residual")
myforecast6 <- forecast(mymodel6, level =c (95), h= 3*12)
plot(myforecast6,main = "Salary forecasting for all Blacks or African Americans in next 3years",xlab = "Time", ylab ="Average salary")
myforecast6[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 116 40385.44
## 117 39884.03 39827.02 39775.68 39729.44 39687.80 39650.30 39616.53
## 118 39473.82 39457.60 39442.99 39429.83 39417.98 39407.30 39397.69
## 119 39357.08 39352.46 39348.30 39344.56 39341.18 39338.15
## Aug Sep Oct Nov Dec
## 116 40278.58 40182.34 40095.67 40017.62 39947.33
## 117 39586.12 39558.73 39534.06 39511.85 39491.84
## 118 39389.04 39381.24 39374.22 39367.90 39362.21
## 119forecasting for race 3: American Indian
library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
Airace <- subset(newrace, race_name == "American Indian",select = c(avg_wage, year,race_name))
head(Airace)Airace$avg_wage <- as.numeric(Airace$avg_wage)
Airace$Date <- paste (Airace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata7 <- ts(Airace$avg_wage,frequency = 12)
tsdata7## Jan Feb Mar Apr May Jun
## 1 44442.700 84248.500 55566.800 32824.100 5573.680 108226.000
## 2 28561.500 10289.900 25724.700 32752.200 22916.400 43580.200
## 3 49198.400 12393.900 42353.900 21054.600 202415.000 102796.000
## 4 95832.200 50196.200 116129.000 41577.200 98891.700 30326.700
## 5 10634.200 11092.700 26219.100 21868.100 32261.200 20833.100
## 6 39083.300 40961.500 52013.600 30413.900 8753.090 58089.000
## 7 35355.300 24389.100 7054.160 27484.400 14461.800 52888.200
## 8 15778.100 10182.700 17178.700 40483.000 62286.200 16412.000
## 9 14939.400 3829.480 25830.900 79424.100 85342.600 44695.900
## 10 21437.200 55923.100 21388.500 15638.700 109414.000 37499.500
## 11 157564.000 81445.300 14189.100 42707.800 92108.500 27017.800
## 12 11613.100 25600.900 50421.200 26827.000 11017.800 87530.900
## 13 30416.400 416.662 2048.070 5120.190 184327.000 53057.100
## 14 93001.600 16692.300 37683.100 31073.500 9374.890 44905.200
## 15 33042.900 17291.500 14889.100 34714.900 25109.800 13480.500
## 16 36558.000 34567.000 25600.900 29406.500 33662.700 35520.400
## 17 3276.920 69423.900 38026.300 36386.400 14748.700 39395.500
## 18 9821.450 120355.000 36205.900 104165.000 196615.000 40595.500
## 19 22314.100 28374.400 37499.500 9173.510 10542.500 5042.120
## 20 39079.500 22185.300 22612.100 7291.580 61796.300 82941.000
## 21 127164.000 70913.900 41217.200 57717.000 108377.000 19192.800
## 22 7299.960 14763.100 100126.000 47705.900 41775.000 76891.100
## 23 29036.700 34754.800 48289.200 134137.000 99191.000 39666.800
## 24 55800.900 80101.100 80101.100 42756.900 50479.200 48060.700
## 25 37100.200 111608.000 19827.500 26450.500 39378.600 31068.500
## 26 35335.500 54507.800 11277.400 5126.080 5580.090 45109.500
## 27 48331.400 20857.000 51508.500 26592.700 32019.400 31119.700
## 28 35044.200 32039.400 36793.500 34079.300 11105.400 27258.800
## 29 15019.000 14208.900 10194.400 1312.460 14110.500 10502.200
## 30 39704.500 12115.000 20980.100 30721.300 38631.400 45069.900
## 31 29132.800 10657.200 32306.700 28694.800 60254.100 103868.000
## 32 30522.200 9067.640 33076.200 22942.700 42367.700 6174.180
## 33 38410.900 65082.200 48060.700 42927.100 32532.500 13636.100
## 34 47280.500 30037.900 12506.200 32432.200 30875.500 49255.000
## 35 23220.500 21962.500 61077.100 30231.200 43096.400 37933.000
## 36 417.141 42581.300 37542.700 26357.900 60356.400 25630.400
## 37 37046.800 99125.100 54317.700 44699.400 26249.200 27999.600
## 38 22666.800 12815.800 36847.300 16403.400 88843.500 45885.500
## 39 54228.300 38819.700 2294.270 63766.700 60826.000 69582.500
## 40 64669.500 113687.000 21529.200 83141.000 186871.000 38092.100
## 41 55023.000 43429.200 77862.800 100759.000 19761.300 97154.800
## 42 30227.600 32242.800 100476.000 36804.300 124088.000 31739.500
## 43 71792.300 48668.000 42938.400 22306.200 57422.600 52773.600
## 44 30819.300 25961.200 60495.400 40268.300 38017.100 22469.000
## 45 41526.800 42812.400 54179.100 99664.300 42241.200 21120.600
## 46 16591.300 16159.400 35487.100 11486.500 35038.800 35270.800
## 47 17763.000 11744.900 5027.190 20196.300 14106.200 2053.200
## 48 19006.200 16896.500 12268.100 25413.200 39170.900 5111.710
## 49 10846.700 32714.900 30667.200 64604.300 114199.000 204477.000
## 50 33104.400 23232.700 31604.600 25433.100 40464.800 18136.600
## 51 65904.500 48668.000 46813.600 40351.300 15827.000 22491.500
## 52 14909.200 30301.900 40201.000 42938.400 21422.300 15943.400
## 53 45439.600 30453.300 43490.200 38412.300 63455.700 30833.600
## 54 38017.100 23924.500 31116.700 36717.500 37957.700 15309.700
## 55 37514.900 100378.000 56779.300 45264.200 26580.900 28899.600
## 56 12561.500 34131.100 16610.700 89966.100 46465.300 15335.100
## 57 54913.600 52801.500 2323.270
## Jul Aug Sep Oct Nov Dec
## 1 48233.700 11264.400 16477.800 13230.000 26057.300 45018.200
## 2 21882.700 33465.400 12684.300 13535.900 32269.600 22633.800
## 3 3875.590 55736.800 7082.980 34117.800 47757.300 10433.500
## 4 37889.400 22448.000 15402.100 35012.400 14091.000 29649.700
## 5 44626.800 13129.600 1310.950 37274.900 25141.300 5104.100
## 6 38587.500 31672.900 42353.900 64179.300 67295.900 54166.000
## 7 27623.400 23713.900 39658.900 31224.800 29721.300 71814.700
## 8 20172.500 25799.200 26466.500 12035.500 47047.800 50421.200
## 9 64582.500 45350.200 71118.800 133983.000 31790.200 26259.300
## 10 32603.800 27227.500 88741.400 76802.800 36561.500 31113.100
## 11 31084.000 19088.100 19977.800 42043.600 76589.500 20638.600
## 12 26420.200 48233.700 47821.900 34139.800 38587.000 44648.000
## 13 45057.600 12101.100 98307.600 23022.500 60609.500 16077.900
## 14 2381.410 13601.400 42124.100 24885.000 18619.600 32269.600
## 15 15317.900 98472.200 52922.000 39825.400 36203.800 59083.300
## 16 96467.500 17781.700 176857.000 63951.000 15626.800 25983.200
## 17 48096.400 389134.000 58551.400 28579.300 100191.000 34472.800
## 18 110785.000 39388.900 11296.400 30955.400 36865.300 48233.700
## 19 70832.500 18228.900 25210.600 38841.800 14106.100 53057.100
## 20 57606.000 121260.000 84249.000 64656.800 177061.000 26875.400
## 21 87010.500 184539.000 35699.400 104285.000 56568.100 32306.700
## 22 157745.000 20000.800 95942.400 19078.100 44055.600 48285.800
## 23 127390.000 53118.200 21113.400 35803.800 87110.000 80101.100
## 24 42402.600 22027.800 48289.200 56181.400 52845.200 196841.000
## 25 29090.300 58524.900 38604.600 48289.200 37542.700 26603.400
## 26 96578.500 41008.600 40246.300 38757.600 65537.100 98420.600
## 27 23596.200 19630.200 32294.500 26965.800 25138.700 13439.900
## 28 25099.700 14728.200 17195.700 16352.000 25754.300 10744.800
## 29 29964.400 17736.200 39817.800 17689.000 19720.300 16685.600
## 30 5047.920 3923.530 15223.900 5531.760 22825.800 32374.800
## 31 43429.100 164948.000 2050.430 19541.500 42648.900 32781.100
## 32 22718.600 78034.100 25031.600 25471.900 27559.300 10764.800
## 33 22210.900 27323.000 49061.900 45775.200 30884.100 16096.400
## 34 42402.600 18699.000 15447.700 17117.300 15378.200 38782.400
## 35 61926.000 30448.900 46048.000 11482.400 27079.200 35852.300
## 36 13859.900 13694.300 28369.300 25630.400 15608.000 36907.700
## 37 22027.800 21724.800 42172.600 24886.400 31260.500 190240.000
## 38 16627.600 5109.970 46134.700 8575.030 16277.000 31285.600
## 39 53674.600 65473.900 27147.000 125447.000 71810.000 40491.800
## 40 105603.000 67040.100 32714.900 7392.210 14949.600 101392.000
## 41 17292.500 44612.300 45187.000 29403.600 85621.100 55417.300
## 42 40232.700 88210.700 81113.300 81113.300 81113.300 43297.200
## 43 71891.000 29877.000 117125.000 20492.300 26784.800 40035.400
## 44 35782.000 50379.400 44879.400 11419.900 5190.850 45679.500
## 45 19079.600 32911.300 34424.900 21935.100 35681.400 24143.000
## 46 31963.400 11245.800 27603.200 18106.200 17039.400 18080.800
## 47 14086.300 9816.340 37028.600 17960.300 35567.100 15239.400
## 48 3857.620 16554.100 4637.250 23436.900 29173.800 29330.600
## 49 127594.000 2076.340 26604.800 43187.900 28753.300 28489.600
## 50 25347.900 26872.700 19844.000 22166.900 10945.300 38711.800
## 51 27121.000 49681.900 70974.100 26061.500 60042.800 30417.500
## 52 25189.700 22672.500 15572.500 38518.000 14796.500 5190.850
## 53 11627.500 26110.300 50592.100 422.412 60455.300 43749.700
## 54 9419.620 54409.800 28727.800 25954.200 15341.500 37374.100
## 55 22306.200 25954.200 22166.900 20517.500 192644.000 4258.450
## 56 30227.600 5174.550 45852.500 8161.190 13817.200 16510.000
## 57ddata7 <- decompose(tsdata7, "multiplicative")
plot(ddata7)
plot(ddata7$seasonal)
plot(ddata7$trend)
plot(Airace$avg_wage,type ="l",main = "Average salary for Americans Indian",xlab= "Year",ylab="Average Salary")
class(tsdata7)## [1] "ts"mymodel7 <- auto.arima(tsdata7)
mymodel7## Series: tsdata7
## ARIMA(1,0,1) with non-zero mean
##
## Coefficients:
## ar1 ma1 mean
## 0.9369 -0.8483 41260.458
## s.e. 0.0341 0.0532 3178.222
##
## sigma^2 estimated as 1.215e+09: log likelihood=-8016.12
## AIC=16040.25 AICc=16040.31 BIC=16058.31auto.arima(tsdata7,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(0,0,0) with non-zero mean : 16078.66
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 16071.34
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 16069.7
## ARIMA(0,0,0) with zero mean : 16647.39
## ARIMA(0,0,1) with non-zero mean : 16067.7
## ARIMA(0,0,1)(1,0,0)[12] with non-zero mean : 16073.5
## ARIMA(0,0,1)(1,0,1)[12] with non-zero mean : 16075.45
## ARIMA(1,0,1) with non-zero mean : 16041
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 16043.98
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 16041.57
## ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 16045.93
## ARIMA(1,0,0) with non-zero mean : 16065.81
## ARIMA(2,0,1) with non-zero mean : 16047
## ARIMA(1,0,2) with non-zero mean : 16042.81
## ARIMA(0,0,2) with non-zero mean : 16062.67
## ARIMA(2,0,0) with non-zero mean : 16058.05
## ARIMA(2,0,2) with non-zero mean : 16044.74
## ARIMA(1,0,1) with zero mean : Inf
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,0,1) with non-zero mean : 16040.25
##
## Best model: ARIMA(1,0,1) with non-zero mean## Series: tsdata7
## ARIMA(1,0,1) with non-zero mean
##
## Coefficients:
## ar1 ma1 mean
## 0.9369 -0.8483 41260.458
## s.e. 0.0341 0.0532 3178.222
##
## sigma^2 estimated as 1.215e+09: log likelihood=-8016.12
## AIC=16040.25 AICc=16040.31 BIC=16058.31library(tseries)
adf.test(mymodel7$residuals)## Warning in adf.test(mymodel7$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel7$residuals
## Dickey-Fuller = -8.2756, Lag order = 8, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel7$residuals)
acf(ts(mymodel7$residuals),main = "ACF Residual")
pacf(ts(mymodel7$residuals),main = "PACF Residual")
myforecast7 <- forecast(mymodel7, level =c (95), h= 3*12)
plot(myforecast7,main = "Salary forecasting for American Indian in next 3years",xlab = "Time", ylab ="Average salary")
myforecast7[["mean"]]## Jan Feb Mar Apr May Jun Jul Aug
## 57 34510.79 34936.44 35335.26 35708.92 36059.02
## 58 37504.89 37741.73 37963.63 38171.54 38366.34 38548.85 38719.85 38880.07
## 59 39541.76 39650.15 39751.70 39846.85 39936.00 40019.52 40097.78 40171.10
## 60 40473.92 40523.52 40569.99
## Sep Oct Nov Dec
## 57 36387.04 36694.37 36982.32 37252.11
## 58 39030.19 39170.84 39302.61 39426.08
## 59 40239.80 40304.16 40364.47 40420.98
## 60forecasting for race 4: Alaska Native
library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
Anrace <- subset(newrace, race_name == "Alaska Native",select = c(avg_wage, year,race_name))
head(Anrace)Anrace$avg_wage <- as.numeric(Anrace$avg_wage)
Anrace$Date <- paste (Anrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata8 <- ts(Anrace$avg_wage,frequency = 12)
tsdata8## Jan Feb Mar Apr May Jun Jul
## 1 122884.00 18151.70 12862.30 126053.00 60024.20 55463.40 21504.80
## 2 172570.00 126198.00 54068.30 172768.00 13016.40 20857.00 45056.90
## 3 10012.60 12877.10 18242.60 21529.50 1001.26 123026.00 37674.50
## 4 30227.60 56228.80 23513.90 18402.20 25954.20 10139.20 21801.60
## Aug Sep Oct Nov Dec
## 1 25600.90 18221.60 20833.10 23193.80 21437.20
## 2 55527.20 23220.50 18172.50 25630.40 21461.90
## 3 127793.00 54751.50 13180.90 21120.60 45626.20
## 4 1013.92 124580.00 23015.90ddata8 <- decompose(tsdata8, "multiplicative")
plot(ddata8)
plot(ddata8$seasonal)
plot(ddata8$trend)
plot(Anrace$avg_wage,type ="l",main = "Average salary for Alaska Native",xlab= "Year",ylab="Average Salary")
class(tsdata8)## [1] "ts"mymodel8 <- auto.arima(tsdata8)
mymodel8## Series: tsdata8
## ARIMA(0,0,0) with non-zero mean
##
## Coefficients:
## mean
## 45575.874
## s.e. 6650.744
##
## sigma^2 estimated as 2.08e+09: log likelihood=-558.24
## AIC=1120.49 AICc=1120.77 BIC=1124.15auto.arima(tsdata8,ic="aic",trace=TRUE)##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
## ARIMA(0,0,0) with non-zero mean : 1120.489
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 1123.793
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 1123.786
## ARIMA(0,0,0) with zero mean : 1150.851
## ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 1122.156
## ARIMA(0,0,0)(0,0,1)[12] with non-zero mean : 1122.096
## ARIMA(0,0,0)(1,0,1)[12] with non-zero mean : 1123.91
## ARIMA(1,0,0) with non-zero mean : 1122.274
## ARIMA(0,0,1) with non-zero mean : 1122.311
## ARIMA(1,0,1) with non-zero mean : 1123.996
##
## Best model: ARIMA(0,0,0) with non-zero mean## Series: tsdata8
## ARIMA(0,0,0) with non-zero mean
##
## Coefficients:
## mean
## 45575.874
## s.e. 6650.744
##
## sigma^2 estimated as 2.08e+09: log likelihood=-558.24
## AIC=1120.49 AICc=1120.77 BIC=1124.15library(tseries)
adf.test(mymodel8$residuals)##
## Augmented Dickey-Fuller Test
##
## data: mymodel8$residuals
## Dickey-Fuller = -3.3059, Lag order = 3, p-value = 0.08263
## alternative hypothesis: stationaryplot.ts(mymodel8$residuals)
acf(ts(mymodel8$residuals),main = "ACF Residual")
pacf(ts(mymodel8$residuals),main = "PACF Residual")
myforecast8 <- forecast(mymodel8, level =c (95), h= 3*12)
plot(myforecast8,main = "Salary forecasting for Alaska Native in next 3years",xlab = "Time", ylab ="Average salary")
myforecast8[["mean"]]## Jan Feb Mar Apr May Jun Jul Aug
## 4
## 5 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87
## 6 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87
## 7 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87
## Sep Oct Nov Dec
## 4 45575.87 45575.87
## 5 45575.87 45575.87 45575.87 45575.87
## 6 45575.87 45575.87 45575.87 45575.87
## 7 45575.87 45575.87forecasting for race 5: Other Native American
library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
onrace <- subset(newrace, race_name == "Other Native American",select = c(avg_wage, year,race_name))
head(onrace)onrace$avg_wage <- as.numeric(onrace$avg_wage)
onrace$Date <- paste (onrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata9 <- ts(onrace$avg_wage,frequency = 12)
tsdata9## Jan Feb Mar Apr May Jun
## 1 57379.200 164120.000 26796.500 51201.900 31619.900 15006.100
## 2 40469.700 17506.200 10718.600 37499.500 8192.300 36737.000
## 3 11187.700 10790.100 21036.800 37515.100 39388.900 93001.600
## 4 120355.000 10941.400 52082.700 20480.700 26215.900 45025.200
## 5 40809.200 25712.200 7717.400 57291.000 45018.200 28454.600
## 6 34413.700 20812.300 44600.500 78124.000 35992.400 41666.200
## 7 15624.800 10588.500 11790.500 1024.040 70905.600 15624.800
## 8 22174.100 20833.100 30721.100 20688.000 7680.280 27339.400
## 9 70658.600 66562.400 82060.200 20905.900 6413.120 52516.200
## 10 28228.700 47791.500 37830.600 6645.540 15541.800 37499.500
## 11 83605.200 63788.000 41879.300 11246.700 30012.100 42353.900
## 12 51260.800 29719.200 39240.000 70739.800 37542.700 56070.800
## 13 64656.800 36045.500 43059.000 10730.900 43278.400 23220.500
## 14 34339.000 48391.300 23509.100 16740.300 36499.800 51488.800
## 15 37558.300 18980.200 41714.100 31656.300 32192.800 36088.700
## 16 14762.500 11804.000 20522.100 18631.200 19320.400 10600.600
## 17 26827.300 47846.500 42402.600 141181.000 30287.600 3204.040
## 18 19507.800 27900.400 37558.300 15535.300 27342.300 30046.600
## 19 16192.500 52142.600 28658.900 31191.800 38234.000 30756.400
## 20 100.126 429542.000 78213.900 7726.280 53654.700 15642.800
## 21 21899.900 42857.900 33369.700 6653.180 45069.900 8201.720
## 22 10428.500 27908.700 10223.400 58081.600 20064.600 51908.500
## 23 17426.600 42241.200 27603.200 62290.200 50695.800 50024.500
## 24 10939.100 23174.500 51908.500 6448.560 125798.000 79109.900
## 25 36961.100 109045.000 67835.700 96728.400 42656.900 37448.600
## 26 18136.600 35515.200 18076.800 10736.300 7429.830 9043.930
## 27 10734.600 17129.000 38017.100 7786.270 42816.900 6458.930
## 28 3244.530 45626.200 17025.300 22823.400 24916.100 20000.700
## 29 62290.200 44641.300 23287.600 15840.500 15894.200 63923.700
## 30 20244.500 121670.000 10075.900 67481.000 32445.300 38017.100
## 31 50695.800 42938.400 20151.800 16628.300 22176.600 33855.000
## 32 49633.400 60835.000 33793.000 10560.300
## Jul Aug Sep Oct Nov Dec
## 1 23193.800 24727.500 15208.500 37889.400 30889.700 10302.800
## 2 8798.640 10084.200 82060.200 64582.500 61442.200 29817.600
## 3 10249.700 13129.600 33463.400 37305.500 36598.800 5173.600
## 4 53593.100 31323.200 11755.600 31817.800 51201.900 17023.900
## 5 61442.200 24576.900 20691.400 23202.400 21261.600 42671.300
## 6 30252.800 20030.400 28878.900 48957.700 10416.500 15809.700
## 7 1041.650 24713.800 31463.000 25724.700 43009.600 65648.200
## 8 41666.200 21874.700 42395.100 24055.900 37515.100 18221.600
## 9 27868.400 51429.700 19892.600 156039.000 27353.400 34299.600
## 10 25165.100 49286.100 25251.900 43228.600 17778.100 16077.900
## 11 10072.300 36457.900 51260.800 10095.800 57356.900 19814.200
## 12 43653.700 83701.300 41714.100 88920.700 61512.900 50063.200
## 13 1025.210 10802.600 51260.800 119561.000 121384.000 41595.600
## 14 68963.900 39279.000 34866.000 27699.400 28073.100 30287.600
## 15 30831.700 20504.300 11058.100 13521.900 9079.520 13060.900
## 16 37542.700 7689.110 30244.600 24725.900 9719.500 17109.000
## 17 15023.300 45056.900 16096.400 23309.100 18108.300 24605.200
## 18 27372.800 20504.300 61512.900 44675.700 27724.800 15642.800
## 19 35720.900 19865.100 120152.000 66639.000 32040.400 37542.700
## 20 37933.000 50063.200 42402.600 22511.700 18242.600 25579.900
## 21 22711.900 20857.000 6323.000 49014.000 60075.800 33371.300
## 22 24570.100 57432.500 46099.300 70751.700 38017.100 56779.300
## 23 36501.000 43603.100 88667.700 43825.200 23513.900 1038.170
## 24 34362.400 50637.200 25215.900 3022.760 64485.600 38288.300
## 25 29403.600 50728.700 30670.300 19220.000 42241.200 24810.300
## 26 15080.200 20278.300 13300.200 10188.200 20753.700 18365.800
## 27 10645.500 49951.100 44983.000 349546.000 70531.200 41532.400
## 28 22166.900 22368.500 16168.700 24495.700 26511.100 20763.400
## 29 28324.600 30499.800 19453.200 35165.800 31145.100 29347.300
## 30 101.392 434970.000 65254.900 24685.900 15840.500 38412.300
## 31 39404.800 30309.200 8305.360 19461.600 21120.600 1557.260
## 32ddata9 <- decompose(tsdata9, "multiplicative")
plot(ddata9)
plot(ddata9$seasonal)
plot(ddata9$trend)
plot(onrace$avg_wage,type ="l",main = "Average salary for Other Native American",xlab= "Year",ylab="Average Salary")
class(tsdata9)## [1] "ts"mymodel9 <- auto.arima(tsdata9)
mymodel9## Series: tsdata9
## ARIMA(0,0,0) with non-zero mean
##
## Coefficients:
## mean
## 37598.30
## s.e. 2125.52
##
## sigma^2 estimated as 1.703e+09: log likelihood=-4529.12
## AIC=9062.24 AICc=9062.27 BIC=9070.1auto.arima(tsdata9,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 9074.159
## ARIMA(0,0,0) with non-zero mean : 9062.236
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 9066.661
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 9064.762
## ARIMA(0,0,0) with zero mean : 9287.899
## ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 9065.127
## ARIMA(0,0,0)(0,0,1)[12] with non-zero mean : 9064.234
## ARIMA(0,0,0)(1,0,1)[12] with non-zero mean : 9067.009
## ARIMA(1,0,0) with non-zero mean : 9063.454
## ARIMA(0,0,1) with non-zero mean : 9062.763
## ARIMA(1,0,1) with non-zero mean : 9064.197
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(0,0,0) with non-zero mean : 9062.236
##
## Best model: ARIMA(0,0,0) with non-zero mean## Series: tsdata9
## ARIMA(0,0,0) with non-zero mean
##
## Coefficients:
## mean
## 37598.30
## s.e. 2125.52
##
## sigma^2 estimated as 1.703e+09: log likelihood=-4529.12
## AIC=9062.24 AICc=9062.27 BIC=9070.1library(tseries)
adf.test(mymodel9$residuals)## Warning in adf.test(mymodel9$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel9$residuals
## Dickey-Fuller = -6.4511, Lag order = 7, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel9$residuals)
acf(ts(mymodel9$residuals),main = "ACF Residual")
pacf(ts(mymodel9$residuals),main = "PACF Residual")
myforecast9 <- forecast(mymodel9, level =c (95), h= 3*12)
plot(myforecast9,main = "Salary forecasting for Other Native American in next 3years",xlab = "Time", ylab ="Average salary")
myforecast9[["mean"]]## Jan Feb Mar Apr May Jun Jul Aug Sep
## 32 37598.3 37598.3 37598.3 37598.3 37598.3
## 33 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3
## 34 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3
## 35 37598.3 37598.3 37598.3 37598.3
## Oct Nov Dec
## 32 37598.3 37598.3 37598.3
## 33 37598.3 37598.3 37598.3
## 34 37598.3 37598.3 37598.3
## 35forecasting for race 6: Asian
library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
asrace <- subset(newrace, race_name == "Asian",select = c(avg_wage, year,race_name))
head(asrace)asrace$avg_wage <- as.numeric(asrace$avg_wage)
asrace$Date <- paste (asrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata10 <- ts(asrace$avg_wage,frequency = 12)
tsdata10## Jan Feb Mar Apr May Jun
## 1 52914.100 26041.300 99637.800 19293.500 64820.600 161415.000
## 2 42411.200 82837.400 27054.900 8499.510 32155.800 32824.100
## 3 58952.400 14201.300 64195.500 27017.000 52518.500 13070.100
## 4 53355.000 50471.400 72095.700 65648.200 34605.000 42435.000
## 5 43135.900 21300.000 22439.500 32538.200 17997.600 28939.000
## 6 53821.100 24289.200 37111.100 49911.000 29046.100 22490.600
## 7 47276.800 80542.000 89303.100 28059.300 70750.400 51951.700
## 8 65648.200 20845.200 22100.100 75902.700 34957.100 20718.100
## 9 5999.350 30012.100 27099.000 22873.600 22981.200 85617.600
## 10 38228.500 35300.100 13231.400 37320.900 4006.750 48668.000
## 11 74366.900 24000.800 37269.800 32916.300 42254.600 35776.200
## 12 89440.600 37671.100 20975.200 25816.600 30323.300 11147.400
## 13 39304.400 58332.600 104452.000 46352.500 36118.200 27099.300
## 14 41930.500 20681.400 42736.000 60485.400 42638.400 49153.800
## 15 27353.400 55736.800 57565.000 69763.100 28143.000 22013.600
## 16 49211.100 27353.400 28217.200 23620.600 26796.500 9959.910
## 17 61708.100 36304.800 40438.600 78052.400 37320.600 34016.300
## 18 22796.700 18880.600 80199.700 31438.800 17377.000 24083.700
## 19 27127.700 23289.400 42206.900 35219.500 17706.100 6088.060
## 20 101827.000 59250.700 90910.900 29803.800 56751.400 73688.000
## 21 22647.900 26705.700 41101.000 37259.300 71179.000 16555.600
## 22 36874.600 75451.300 36155.800 31797.400 33186.300 18711.600
## 23 5042.120 26796.500 16100.200 95321.400 59361.400 11790.500
## 24 24845.500 42748.100 55750.100 6041.590 67121.000 71170.300
## 25 14270.100 73997.600 21261.200 16081.300 35200.800 25535.900
## 26 83531.300 145832.000 88119.000 16345.900 13184.300 11045.600
## 27 6249.920 57251.300 53498.900 44399.400 45211.200 51563.600
## 28 19293.500 80861.200 50629.800 59456.600 51069.600 75892.100
## 29 29388.300 14223.800 67603.000 21026.000 37719.600 8627.920
## 30 34266.700 47980.400 32672.500 31632.700 56657.300 16297.300
## 31 77822.800 53971.100 57024.300 26607.100 82060.200 19320.700
## 32 19203.800 26435.200 11716.700 51697.400 23817.000 50360.500
## 33 12862.300 26867.300 49903.200 48296.400 34028.600 9279.630
## 34 45180.300 31976.700 29285.700 36095.100 23451.000 33800.100
## 35 76613.800 59799.100 191568.000 44674.700 135571.000 98708.700
## 36 55800.900 53212.700 91476.000 67562.800 52624.900 52595.700
## 37 49603.200 81933.000 44537.000 28398.400 68896.900 8839.600
## 38 50801.700 45860.600 74174.200 82969.600 62892.700 121172.000
## 39 77119.100 74099.800 38944.200 75446.200 86266.400 66154.300
## 40 95351.400 64575.500 53042.300 74038.100 71538.200 84112.000
## 41 145999.000 70744.500 49076.700 43051.300 46215.100 57155.300
## 42 28035.400 44930.100 41295.700 48883.300 88262.500 47385.700
## 43 33967.500 3836.280 20800.700 31091.300 83699.400 41118.000
## 44 13752.000 31809.200 20343.400 11856.300 14425.200 45296.500
## 45 25630.400 85063.100 30372.300 38925.600 30997.000 9043.090
## 46 88134.200 217811.000 76896.000 12463.400 62003.800 69379.500
## 47 104039.000 59020.200 41039.500 72879.400 49031.100 44995.200
## 48 53052.900 12302.600 26363.900 54068.300 31234.000 34875.000
## 49 27539.900 58399.700 100471.000 104572.000 21250.100 91742.000
## 50 34327.600 2885.650 28504.900 55522.700 22310.200 15436.100
## 51 9962.740 15708.200 29449.700 31380.100 18304.600 18696.400
## 52 52941.900 16268.500 16461.300 9385.670 22996.400 19116.200
## 53 15870.500 39860.900 67375.200 18351.800 20054.400 22036.500
## 54 58215.600 46066.300 97275.700 24705.700 363.451 48810.900
## 55 43994.600 19315.700 21690.900 37031.200 24030.300 27246.100
## 56 17144.900 49411.400 11804.000 33966.700 22098.700 44344.800
## 57 42003.400 41046.800 45453.700 26483.200 18575.500 31774.400
## 58 25735.800 30741.800 31977.900 24857.100 25553.800 64993.000
## 59 2120.130 40715.800 36011.500 31083.800 25754.300 33879.500
## 60 18771.300 43987.500 30517.400 48000.100 32954.100 45353.100
## 61 4005.060 20299.300 42467.700 70088.500 39191.600 61512.900
## 62 56945.000 26924.500 24585.400 27276.500 55262.600 30915.100
## 63 48220.000 36804.800 30263.000 43674.200 30405.300 27969.100
## 64 55527.200 71765.000 26794.700 57840.300 31877.300 30046.600
## 65 26561.900 39898.600 19315.700 26827.300 32234.400 24004.000
## 66 21102.900 27013.700 23855.600 17740.300 11927.900 16751.700
## 67 26322.800 25995.000 36917.000 109308.000 117795.000 16500.200
## 68 101005.000 90141.100 69542.200 169836.000 37429.200 137284.000
## 69 45727.500 47252.700 81488.700 67663.300 54809.800 38660.400
## 70 48710.400 82902.600 49682.800 31022.200 65921.000 49681.900
## 71 55930.900 44920.700 74796.600 100253.000 84410.400 122831.000
## 72 78382.700 75496.500 41524.900 81210.900 87133.500 68695.800
## 73 98889.000 20151.800 67882.200 58571.600 74852.500 78398.400
## 74 61636.200 147844.000 90781.800 51547.000 43926.400 48577.400
## 75 47305.000 59493.100 28389.600 53721.400 54190.800 35782.000
## 76 36273.200 43838.900 34927.700 31132.100 2230.620 22894.300
## 77 33996.400 34241.100 45825.700 10501.800 34281.400 29783.000
## 78 19560.200 25878.300 42597.700 25954.200 84968.100 26848.600
## 79 166484.000 54148.900 68873.300 89357.100 219873.000 90397.300
## 80 47573.400 92895.800 63117.700 95427.300 63478.000 46459.300
## 81 35033.000 53779.400 10020.100 60727.400 42157.700 26620.900
## 82 45670.600 20128.200 22199.000 59137.700 101741.000 105893.000
## 83 40552.800 29918.500 4665.110 32616.000 3003.820 28317.800
## 84 18705.300 15264.800 15977.500 8611.420 12686.500 28943.800
## 85 42915.700 21636.600 44039.600 16474.100 14587.300 12706.600
## 86 22974.700 5330.330 17222.000 42018.300 68991.300 18017.600
## 87 35621.200 62776.900 50832.300 43069.900 96552.300 21440.900
## 88 30586.500 33828.000 45077.100 38288.300 24434.600 35996.400
## 89 11998.500 58030.600 19960.900 55341.400 34856.700 22250.100
## 90 57724.100 57117.600 37457.100 50023.800 22236.400 20431.800
## 91 17578.700 25907.100 23283.700 32279.500 30227.600 23513.900
## 92 28313.000 24286.800 2146.920 40734.600 30844.900 29785.100
## 93 19008.500 95609.100 32854.400 48279.500 60455.300 33370.500
## 94 53263.000 4055.660 20555.800 38288.300 48694.700 70974.100
## 95 38951.200 35156.700 58354.100 27384.000 25454.000 27215.400
## 96 26302.500 45604.500 37939.600 20151.800 30519.500 44226.000
## 97 20487.600 20028.100 6045.530 9684.370 56228.800 72671.900
## 98 34947.300 27844.300 34166.600 25529.200 36183.800 33252.900
## 99 70464.900 8616.810 20276.400 26017.200 31196.200 18973.600
## 100 19874.600 22421.300 30078.300 26727.300 37383.500 112964.000
## Jul Aug Sep Oct Nov Dec
## 1 29192.100 56351.900 136108.000 86738.500 55879.600 37274.900
## 2 58794.700 89133.700 104414.000 43154.800 25311.600 41939.400
## 3 32678.400 24490.300 10077.100 27618.800 36196.700 31970.700
## 4 49236.100 45147.400 47161.900 17779.400 57543.200 30063.600
## 5 67707.500 52598.900 125508.000 8950.310 63540.900 73078.400
## 6 35416.200 97593.500 46839.500 57480.800 72886.500 95665.500
## 7 75588.800 37012.200 55255.400 22326.000 30691.500 68379.900
## 8 6997.290 33008.700 26968.900 36457.900 29333.800 51648.900
## 9 27079.000 42694.800 137452.000 48982.400 82975.200 48676.000
## 10 28275.300 14967.000 109414.000 35520.800 60328.100 81502.200
## 11 48484.600 30721.100 38893.000 14661.400 39697.000 46203.400
## 12 24125.000 83492.500 124233.000 80635.600 94479.700 110170.000
## 13 34523.600 48248.700 68280.000 56845.600 41613.500 100356.000
## 14 21998.900 16400.200 27078.000 19101.200 46193.300 12288.400
## 15 22101.000 24071.000 47018.200 24868.300 22276.300 33719.200
## 16 11189.100 25993.000 104251.000 50648.500 40699.300 71540.100
## 17 66900.900 65515.700 41159.100 16554.000 34292.900 32597.900
## 18 35256.800 7398.770 61258.200 38421.000 53640.400 48129.700
## 19 49085.600 75341.800 73615.000 57994.800 78569.900 22052.200
## 20 48887.300 32863.600 914.926 15634.300 109353.000 57596.000
## 21 14637.900 50965.300 40121.200 36212.100 21261.400 85857.400
## 22 31911.800 25987.000 48940.800 20275.900 9409.570 30197.800
## 23 59214.100 4299.230 105174.000 74149.900 26153.900 63804.600
## 24 16128.400 47690.600 61971.100 21378.000 58967.200 40097.000
## 25 32151.400 39167.800 12441.800 45609.300 17261.100 24697.200
## 26 51103.700 48937.000 70598.700 74674.300 214449.000 36790.000
## 27 71682.600 39629.000 16848.100 21759.300 25962.100 71385.800
## 28 43010.400 194698.000 56162.000 40045.600 48736.300 20898.300
## 29 56363.300 22768.000 26086.800 25724.700 2457.690 212932.000
## 30 29747.200 20910.700 27857.700 30176.600 50494.200 53385.400
## 31 15751.600 16412.000 36012.400 46298.400 24447.000 31660.000
## 32 41483.500 17866.500 43624.000 33223.900 75417.400 55812.500
## 33 23228.900 16249.800 58626.100 32155.800 18299.100 57195.800
## 34 78501.900 124900.000 176622.000 84397.400 64513.500 96968.500
## 35 72242.600 9623.260 80493.200 66845.200 247168.000 46847.400
## 36 26190.300 86052.500 63614.000 37911.600 42832.000 55005.900
## 37 13854.800 41818.700 62534.800 56028.400 103335.000 51610.700
## 38 49775.800 33083.500 20216.300 39702.200 72970.400 75944.800
## 39 95926.100 53408.800 76704.400 100666.000 68817.200 69607.400
## 40 90704.100 74619.800 70577.500 105496.000 88302.200 57889.400
## 41 36149.300 71232.400 48492.700 49486.900 31789.600 54339.300
## 42 70068.400 19792.000 29128.300 57702.200 51097.500 40621.500
## 43 41351.600 55433.600 16393.600 35199.800 30553.700 40903.400
## 44 51501.600 23760.700 78444.200 21780.700 23888.800 47282.800
## 45 60575.100 45066.100 63614.000 142046.000 53300.900 60205.900
## 46 55959.700 51220.200 55244.700 50233.400 124760.000 65007.300
## 47 47952.800 36410.300 23193.800 33978.600 55653.900 6048.540
## 48 26719.300 30044.000 48289.200 21060.000 32192.800 13841.500
## 49 34274.500 83784.200 60589.500 40048.200 29292.500 9226.930
## 50 27403.300 22065.700 7989.510 18559.800 14546.100 17475.600
## 51 52065.700 19281.300 31346.400 50077.900 32192.800 4292.380
## 52 15470.300 31761.200 9530.590 27411.100 25730.400 8285.180
## 53 29058.300 43123.100 69966.900 34522.100 39906.200 56234.700
## 54 53576.600 48778.900 35457.000 34307.500 30870.800 34587.500
## 55 25800.000 28090.700 39736.100 21807.700 11719.500 45388.000
## 56 39784.300 23803.200 21324.500 27546.200 27271.800 56198.600
## 57 31480.900 31813.000 26107.500 19600.700 40489.200 17070.300
## 58 48185.100 30394.700 20306.700 34086.500 28660.300 25915.600
## 59 16560.700 46010.900 11160.200 26827.300 20999.400 22027.800
## 60 16853.200 44304.300 36023.800 30777.300 46217.300 49267.700
## 61 62325.400 40414.200 30147.300 30931.500 38465.200 35595.200
## 62 49210.300 22890.400 26850.500 47760.600 28973.500 26072.000
## 63 28734.500 18193.400 26508.100 21753.000 20349.900 9388.050
## 64 19201.600 32806.900 36579.500 27059.500 33961.500 25463.900
## 65 30886.200 38886.600 81154.800 37673.100 69510.500 8509.280
## 66 26837.400 12877.100 36499.800 36348.100 21445.700 23210.100
## 67 62552.000 77908.700 123282.000 349546.000 74184.700 56691.800
## 68 112806.000 74127.800 9393.550 67689.000 61300.000 218212.000
## 69 26450.100 84341.800 64417.800 37450.600 47152.800 60146.700
## 70 14187.100 40556.600 86239.700 52490.300 101510.000 54282.300
## 71 78418.000 30346.500 28386.900 54932.500 121525.000 76768.700
## 72 97215.100 54638.900 79139.700 72648.600 74037.800 73963.900
## 73 86810.300 82948.000 73995.700 87416.200 106866.000 83817.100
## 74 47962.600 32299.200 64760.200 191442.000 49105.400 57923.900
## 75 89142.700 50013.600 70721.600 39291.700 31214.500 55486.300
## 76 41290.800 86522.300 42739.500 41098.200 54244.000 18513.900
## 77 17029.200 14706.600 35736.100 41446.600 24179.800 83692.400
## 78 47185.200 33411.000 8877.540 61340.500 45371.700 64417.800
## 79 22961.400 64974.500 78447.800 56296.600 54982.900 55934.100
## 80 70158.900 52781.900 47867.100 49755.900 37839.200 23329.000
## 81 54751.500 31076.300 35060.800 26109.400 29374.100 16598.100
## 82 25110.900 96888.400 34282.200 84526.200 64015.600 30227.600
## 83 49728.500 23159.000 15255.800 24466.700 21259.900 8113.750
## 84 31566.800 20299.400 19640.000 52723.600 25145.300 31960.900
## 85 23212.700 20154.000 19665.000 27325.900 9200.380 18175.900
## 86 24093.100 19785.400 34452.400 38390.100 93115.800 30142.200
## 87 368.043 48352.100 53613.400 29790.500 35905.000 35911.200
## 88 24334.000 23668.700 22665.200 29661.100 35413.600 23384.700
## 89 45789.300 42938.600 24160.600 21593.900 32549.200 14912.700
## 90 34203.600 30843.600 38955.300 28676.500 21474.400 39642.900
## 91 29482.700 76655.600 48794.000 33983.900 21956.600 36884.900
## 92 34635.400 16849.600 49062.100 35668.600 21264.700 29297.600
## 93 41526.800 21961.300 46495.600 32036.300 30572.300 49915.400
## 94 37737.900 62290.200 86272.900 30567.500 31650.100 26400.800
## 95 41395.000 31024.400 49832.200 23550.800 28351.200 47756.700
## 96 33206.700 28477.200 31045.200 18379.400 24955.400 21221.800
## 97 33587.100 58475.800 25889.800 19396.800 33221.400 54409.800
## 98 32759.600 24619.200 30081.600 37960.200 92000.700 38546.500
## 99 15610.100 13891.500 20648.500 35265.600 36961.100 38776.300
## 100 18097.200 63744.000ddata10 <- decompose(tsdata10, "multiplicative")
plot(ddata10)
plot(ddata10$seasonal)
plot(ddata10$trend)
plot(asrace$avg_wage,type ="l",main = "Average salary for Asian",xlab= "Year",ylab="Average Salary")
class(tsdata10)## [1] "ts"mymodel10 <- auto.arima(tsdata10)
mymodel10## Series: tsdata10
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 sar1 mean
## 0.9390 -0.7889 -0.0206 45460.386
## s.e. 0.0176 0.0311 0.0300 2793.969
##
## sigma^2 estimated as 829756492: log likelihood=-13976.16
## AIC=27962.32 AICc=27962.37 BIC=27987.75auto.arima(tsdata10,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 27948.98
## ARIMA(0,0,0) with non-zero mean : 28159.26
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 28034.35
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 28077.88
## ARIMA(0,0,0) with zero mean : 29508.48
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 27967.68
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 27946.98
## ARIMA(2,0,2) with non-zero mean : 27966.12
## ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 27944.97
## ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 27946.6
## ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 27945.57
## ARIMA(2,0,1)(2,0,0)[12] with non-zero mean : 27945.28
## ARIMA(3,0,2)(2,0,0)[12] with non-zero mean : 27946.56
## ARIMA(2,0,3)(2,0,0)[12] with non-zero mean : 27945.11
## ARIMA(1,0,1)(2,0,0)[12] with non-zero mean : 27944.51
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 27942.35
## ARIMA(1,0,1) with non-zero mean : 27961.03
## ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 27943.94
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 27962.55
## ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 27945.85
## ARIMA(0,0,1)(1,0,0)[12] with non-zero mean : 28057.75
## ARIMA(2,0,1)(1,0,0)[12] with non-zero mean : 27948.09
## ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 27942.97
## ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 28126.27
## ARIMA(0,0,2)(1,0,0)[12] with non-zero mean : 28030.17
## ARIMA(2,0,0)(1,0,0)[12] with non-zero mean : 28002.83
## ARIMA(1,0,1)(1,0,0)[12] with zero mean : Inf
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 27962.32
##
## Best model: ARIMA(1,0,1)(1,0,0)[12] with non-zero mean## Series: tsdata10
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 sar1 mean
## 0.9390 -0.7889 -0.0206 45460.386
## s.e. 0.0176 0.0311 0.0300 2793.969
##
## sigma^2 estimated as 829756492: log likelihood=-13976.16
## AIC=27962.32 AICc=27962.37 BIC=27987.75library(tseries)
adf.test(mymodel10$residuals)## Warning in adf.test(mymodel10$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel10$residuals
## Dickey-Fuller = -10.957, Lag order = 10, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel10$residuals)
acf(ts(mymodel10$residuals),main = "ACF Residual")
pacf(ts(mymodel10$residuals),main = "PACF Residual")
myforecast10 <- forecast(mymodel10, level =c (95), h= 3*12)
plot(myforecast10,main = "Salary forecasting for Asian in next 3years",xlab = "Time", ylab ="Average salary")
myforecast10[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 100
## 101 45959.80 45909.02 45752.85 45823.38 45605.24 44049.38 46005.14
## 102 45437.06 45438.91 45442.87 45442.12 45447.27 45479.94 45440.23
## 103 45454.74 45455.08 45455.35 45455.69 45455.89 45455.51 45456.60
## Aug Sep Oct Nov Dec
## 100 45935.92 45636.94 45604.05 45568.57
## 101 45065.84 45433.83 45441.01 45442.65 45444.28
## 102 45460.12 45453.05 45453.39 45453.80 45454.19
## 103 45456.45forecasting for race 7: Native Hawaiian or Other Pacific Islander
library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
nhrace <- subset(newrace, race_name == "Native Hawaiian or Other Pacific Islander",select = c(avg_wage, year,race_name))
head(nhrace)nhrace$avg_wage <- as.numeric(nhrace$avg_wage)
nhrace$Date <- paste (nhrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata11 <- ts(nhrace$avg_wage,frequency = 12)
tsdata11## Jan Feb Mar Apr May Jun
## 1 73957.400 81923.000 100842.000 76589.500 131296.000 69671.000
## 2 64311.700 27353.400 52518.500 18073.900 34373.800 7373.070
## 3 85716.100 50600.600 82533.300 88540.600 16068.300 7291.580
## 4 27353.400 20243.600 115978.000 12316.000 32291.300 44471.500
## 5 28065.900 14999.800 21437.200 29541.700 74774.700 33135.900
## 6 9075.830 20686.400 65529.800 344787.000 18241.100 46188.000
## 7 18739.900 22686.900 361065.000 21938.300 67355.800 32155.800
## 8 2178.950 28124.700 129635.000 57346.100 61177.800 55093.200
## 9 17408.600 56765.700 7038.150 26827.300 25600.900 29547.200
## 10 60418.200 29244.300 15696.300 47174.000 26470.100 12807.000
## 11 82017.200 41714.100 75226.400 88642.400 29427.800 71894.000
## 12 20025.300 60487.700 25630.400 64992.000 69751.100 78213.900
## 13 32192.800 50161.200 6257.900 50063.200 130404.000 15019.000
## 14 20351.200 32040.400 34343.400 34339.000 63592.800 26839.800
## 15 820.172 32328.400 24147.300 16394.300 28157.000 25031.600
## 16 19897.300 21063.800 10637.100 7139.220 100958.000 22856.900
## 17 36499.800 29271.200 807.668 19024.000 31414.000 7299.960
## 18 29641.400 65082.200 82628.200 45056.900 512.607 57412.000
## 19 16020.200 29378.900 15017.100 21461.900 32119.800 74359.600
## 20 24230.000 98939.300 43960.800 24998.200 39628.400 12821.700
## 21 30227.600 83053.600 60455.300 42241.200 76650.100 89762.500
## 22 75702.400 30417.500 86899.100 20278.300 61252.000 90682.900
## 23 63361.800 22003.700 40041.100 50608.200 25794.300 50795.000
## 24 66105.300 26635.500 50094.800 41526.800 30227.600 21902.500
## 25 51908.500 37583.000 13929.800 12091.100 14160.500 3376.140
## 26 13728.400 4562.620 35496.000 12796.400 1022.340 17648.900
## 27 44574.100 35487.100 28716.300 40893.700 40129.100 36961.100
## 28 52387.300 17340.600 11027.600 30670.300 30628.400 8618.290
## 29 58137.500 60455.300 45085.300 51317.400 60311.900 60455.300
## 30 84111.300 19656.200 40129.100 9725.000 29647.900 5725.120
## 31 12983.700 10381.700 6906.460 10900.800 73922.100
## Jul Aug Sep Oct Nov Dec
## 1 32155.800 34299.600 10798.100 40337.000 39582.900 40730.700
## 2 78124.000 89749.800 10240.400 99108.500 8682.070 22610.600
## 3 28348.700 50103.600 7291.580 25152.100 40699.500 25210.600
## 4 27721.100 36063.400 39105.400 22294.700 29170.300 61442.200
## 5 41666.200 19498.700 17173.300 25005.600 5647.180 44289.600
## 6 32701.300 13541.500 819.230 17322.400 10941.400 70232.800
## 7 98825.600 512.018 31387.600 10624.900 35371.400 54706.800
## 8 84842.700 18151.700 72213.000 40337.000 43967.300 24017.600
## 9 14336.500 27093.200 16412.000 18432.700 29345.200 40483.000
## 10 39582.900 10173.500 30252.800 31133.600 97342.700 7381.550
## 11 89853.100 47059.400 345184.000 74727.200 30037.900 85814.700
## 12 7299.960 31635.600 77483.900 21602.100 40233.800 47127.800
## 13 29278.000 56136.600 52796.200 25239.600 49469.700 41008.600
## 14 9086.260 36814.200 13354.500 22320.400 8692.060 12115.000
## 15 13557.100 4505.690 23579.900 1009.590 17428.700 26902.800
## 16 44017.900 35044.200 40383.400 39628.400 40777.600 35412.100
## 17 49868.600 16965.400 15066.500 30287.600 31721.400 8510.740
## 18 64385.600 44522.700 50725.700 40383.400 14353.000 32192.800
## 19 18994.200 32451.900 9902.510 29278.000 5653.680 25630.400
## 20 10252.200 21911.400 12542.000 72999.600 99868.500 75596.700
## 21 28704.200 96728.400 48276.400 90988.400 47654.100 313544.000
## 22 25954.200 64759.000 79202.200 29220.100 11460.200 28861.100
## 23 6675.620 429232.000 64880.900 128180.000 15208.700 29647.900
## 24 32445.300 31176.500 40303.500 42241.200 20470.400 9201.080
## 25 32736.900 22349.400 20283.400 28512.800 25347.900 3325.040
## 26 24282.300 19997.800 15859.600 10771.500 6722.280 102234.000
## 27 24777.100 817.874 20730.900 28212.500 23851.900 6681.930
## 28 35102.100 3526.560 15845.500 35265.600 45626.200 10048.900
## 29 14534.400 40303.500 16222.700 29750.200 15206.800 34551.100
## 30 25954.200 24536.200 100190.000 41928.900 26664.600 40129.100
## 31ddata11 <- decompose(tsdata11, "multiplicative")
plot(ddata11)
plot(ddata11$seasonal)
plot(ddata11$trend)
plot(nhrace$avg_wage,type ="l",main = "Average salary for Native Hawaiian or Other Pacific Islander",xlab= "Year",ylab="Average Salary")
class(tsdata11)## [1] "ts"mymodel11 <- auto.arima(tsdata11)
mymodel11## Series: tsdata11
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 sma1 mean
## 0.8960 -0.8289 0.0826 41985.410
## s.e. 0.0869 0.1083 0.0554 4162.487
##
## sigma^2 estimated as 2.062e+09: log likelihood=-4430.02
## AIC=8870.03 AICc=8870.2 BIC=8889.53auto.arima(tsdata11,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 8879.224
## ARIMA(0,0,0) with non-zero mean : 8876.082
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 8880.256
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 8875.274
## ARIMA(0,0,0) with zero mean : 9093.531
## ARIMA(0,0,1) with non-zero mean : 8877.038
## ARIMA(0,0,1)(1,0,1)[12] with non-zero mean : 8880.604
## ARIMA(0,0,1)(0,0,2)[12] with non-zero mean : 8877.1
## ARIMA(0,0,1)(1,0,0)[12] with non-zero mean : 8879.49
## ARIMA(0,0,1)(1,0,2)[12] with non-zero mean : 8882.554
## ARIMA(0,0,0)(0,0,1)[12] with non-zero mean : 8873.851
## ARIMA(0,0,0)(1,0,1)[12] with non-zero mean : 8878.79
## ARIMA(0,0,0)(0,0,2)[12] with non-zero mean : 8875.698
## ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 8877.761
## ARIMA(0,0,0)(1,0,2)[12] with non-zero mean : 8880.755
## ARIMA(1,0,0)(0,0,1)[12] with non-zero mean : 8875.667
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 8868.253
## ARIMA(1,0,1) with non-zero mean : 8868.637
## ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 8877.001
## ARIMA(1,0,1)(0,0,2)[12] with non-zero mean : 8869.9
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 8876.403
## ARIMA(1,0,1)(1,0,2)[12] with non-zero mean : 8878.893
## ARIMA(2,0,1)(0,0,1)[12] with non-zero mean : 8868.962
## ARIMA(1,0,2)(0,0,1)[12] with non-zero mean : 8868.986
## ARIMA(0,0,2)(0,0,1)[12] with non-zero mean : 8870.921
## ARIMA(2,0,0)(0,0,1)[12] with non-zero mean : 8870.792
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 8870.89
## ARIMA(1,0,1)(0,0,1)[12] with zero mean : Inf
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 8870.035
##
## Best model: ARIMA(1,0,1)(0,0,1)[12] with non-zero mean## Series: tsdata11
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 sma1 mean
## 0.8960 -0.8289 0.0826 41985.410
## s.e. 0.0869 0.1083 0.0554 4162.487
##
## sigma^2 estimated as 2.062e+09: log likelihood=-4430.02
## AIC=8870.03 AICc=8870.2 BIC=8889.53library(tseries)
adf.test(mymodel11$residuals)## Warning in adf.test(mymodel11$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel11$residuals
## Dickey-Fuller = -6.8276, Lag order = 7, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel11$residuals)
acf(ts(mymodel11$residuals),main = "ACF Residual")
pacf(ts(mymodel11$residuals),main = "PACF Residual")
myforecast11 <- forecast(mymodel11, level =c (95), h= 3*12)
plot(myforecast11,main = "Salary forecasting for Native Hawaiian or Other Pacific Islander in next 3years",xlab = "Time", ylab ="Average salary")
myforecast11[["mean"]]## Jan Feb Mar Apr May Jun Jul Aug
## 31 35232.38 37607.71 37634.66
## 32 37635.37 38037.31 37758.27 38438.15 43638.12 40718.99 40850.64 40968.61
## 33 41398.08 41459.13 41513.84 41562.86 41606.79 41646.15 41681.42 41713.02
## 34 41828.07 41844.43 41859.08 41872.21 41883.98
## Sep Oct Nov Dec
## 31 44355.46 39731.67 38818.25 39996.52
## 32 41074.31 41169.02 41253.89 41329.94
## 33 41741.34 41766.71 41789.44 41809.82
## 34forecasting for race 8: Two or more races
library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
trace <- subset(newrace, race_name == "Two or more races",select = c(avg_wage, year,race_name))
head(trace)trace$avg_wage <- as.numeric(trace$avg_wage)
trace$Date <- paste (trace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata12 <- ts(trace$avg_wage,frequency = 12)
tsdata12## Jan Feb Mar Apr May
## 1 49198.9000 51690.6000 42973.7000 44766.0000 95088.9000
## 2 18464.3000 50983.0000 74983.6000 1466.7400 34849.6000
## 3 25114.5000 41063.1000 20205.5000 8665.7400 22885.3000
## 4 31576.6000 69790.8000 39696.7000 28449.1000 54186.6000
## 5 96135.4000 11673.5000 20168.5000 82816.1000 46895.7000
## 6 44054.5000 43765.6000 77374.7000 52225.9000 132140.0000
## 7 35311.4000 22645.9000 49903.7000 41577.2000 26796.5000
## 8 22492.5000 69671.0000 57271.7000 3958.2900 36987.8000
## 9 121638.0000 35985.5000 76376.7000 42975.4000 29613.9000
## 10 55736.8000 49153.8000 31530.4000 58261.5000 26689.3000
## 11 37646.6000 85748.9000 47661.7000 12101.1000 33853.8000
## 12 17143.2000 84445.1000 10718.6000 25183.5000 52438.1000
## 13 20951.1000 36307.3000 26173.0000 45403.4000 69744.9000
## 14 38062.3000 19584.8000 15283.3000 28528.8000 17022.2000
## 15 54426.6000 8094.1800 21221.2000 20103.1000 6249.9200
## 16 26215.9000 10145.7000 15594.2000 40476.8000 38992.4000
## 17 43319.4000 25407.6000 33091.0000 43417.6000 42510.2000
## 18 20113.5000 20059.3000 5405.7700 38211.4000 35094.9000
## 19 15190.3000 24600.7000 32269.9000 68612.2000 35246.8000
## 20 24777.6000 29559.6000 33142.0000 40763.9000 20285.2000
## 21 10663.8000 161348.0000 8036.4000 24443.5000 31910.8000
## 22 30852.6000 17092.9000 9374.8900 21716.6000 21566.9000
## 23 60109.8000 57291.0000 14438.9000 135441.0000 158973.0000
## 24 66885.2000 15360.6000 54856.7000 45178.2000 32039.2000
## 25 37201.5000 15968.8000 32298.2000 41663.3000 6855.4100
## 26 19688.1000 35012.4000 31764.5000 66745.3000 118310.0000
## 27 64917.5000 49656.6000 38541.2000 21806.7000 19998.2000
## 28 57538.9000 53266.1000 24196.7000 56536.7000 84180.6000
## 29 44834.3000 2606.8500 53802.5000 19407.7000 36335.9000
## 30 25250.7000 19057.6000 39683.9000 48847.1000 22188.3000
## 31 46782.3000 38615.8000 25906.4000 18216.4000 21437.2000
## 32 16841.9000 26802.8000 29864.6000 17392.0000 27349.8000
## 33 36608.9000 35351.4000 50970.5000 80966.1000 3129.5000
## 34 25999.1000 33270.2000 59555.8000 83888.8000 45715.2000
## 35 40089.1000 53654.7000 36853.9000 45625.9000 121492.0000
## 36 50085.7000 54248.6000 33892.7000 63273.1000 16750.6000
## 37 85796.8000 30854.4000 13353.8000 69751.1000 39457.1000
## 38 35661.0000 41496.0000 30255.4000 7517.5500 15378.2000
## 39 76253.9000 88666.8000 49525.6000 55335.2000 53067.0000
## 40 33532.7000 73879.3000 41652.3000 107309.0000 79183.8000
## 41 50216.7000 41269.9000 77123.9000 49210.3000 57978.9000
## 42 16706.1000 35596.8000 33574.0000 11414.4000 143107.0000
## 43 23860.1000 1436.6800 16267.0000 4830.1600 17569.1000
## 44 14814.4000 74967.5000 57356.9000 28405.3000 32235.2000
## 45 92666.0000 29320.5000 48060.7000 85263.5000 261509.0000
## 46 137893.0000 6257.1100 153588.0000 30410.2000 49239.2000
## 47 21868.7000 25963.5000 19506.1000 19343.6000 26765.5000
## 48 86482.0000 60644.5000 35653.1000 57072.6000 56755.3000
## 49 21908.8000 15967.2000 13432.5000 23985.8000 12582.5000
## 50 54381.6000 13801.5000 19554.7000 22629.9000 22114.0000
## 51 12115.0000 11632.4000 18239.0000 18570.6000 25215.7000
## 52 73830.0000 10254.7000 29488.1000 35171.9000 18546.4000
## 53 48812.3000 19987.7000 17827.8000 26031.2000 37228.1000
## 54 25294.3000 63751.3000 32966.1000 30037.9000 21018.2000
## 55 19810.7000 44168.1000 23709.8000 52116.9000 24226.5000
## 56 16470.4000 11505.3000 32840.9000 42842.0000 22768.7000
## 57 9619.7900 30037.9000 7762.0400 20142.7000 12302.6000
## 58 36238.1000 30742.7000 26312.1000 53757.8000 26827.3000
## 59 25202.6000 55832.5000 82052.7000 10730.9000 27427.7000
## 60 34470.6000 20793.4000 28469.9000 25396.6000 61360.5000
## 61 35257.4000 34240.3000 25956.4000 56641.9000 28706.0000
## 62 16099.4000 27189.0000 20813.6000 25681.9000 19096.8000
## 63 12619.8000 11466.9000 15749.5000 18662.6000 16096.4000
## 64 33717.3000 42278.0000 31468.0000 8201.7200 17162.9000
## 65 23925.2000 37046.8000 52018.0000 112642.0000 31868.6000
## 66 55271.2000 16273.1000 16248.0000 74299.8000 10016.3000
## 67 26827.3000 24507.0000 72999.6000 64545.1000 24190.3000
## 68 75073.3000 61150.2000 56794.6000 56839.4000 53962.5000
## 69 40597.3000 100702.0000 22294.4000 60220.2000 50495.7000
## 70 76084.1000 54452.6000 44875.0000 36443.1000 83954.4000
## 71 32102.3000 70754.0000 61179.5000 85369.9000 45049.3000
## 72 66523.7000 64385.0000 90960.3000 65264.3000 77786.4000
## 73 91190.7000 78263.0000 149769.0000 73018.9000 41447.5000
## 74 34441.4000 73113.9000 92561.1000 50985.8000 45476.7000
## 75 29779.8000 29489.4000 31552.4000 38140.2000 29068.8000
## 76 43930.7000 47054.7000 20328.6000 38239.5000 42558.1000
## 77 8007.2100 29961.4000 56912.4000 30367.0000 16619.3000
## 78 26790.3000 19682.6000 25637.5000 41145.4000 47322.7000
## 79 61894.4000 8564.5000 54706.1000 70368.1000 48708.2000
## 80 37925.1000 23073.6000 30180.2000 39337.4000 55092.9000
## 81 29110.5000 15164.1000 12091.1000 28012.8000 27046.4000
## 82 59006.8000 6625.9600 87664.4000 31301.3000 4012.9100
## 83 13214.9000 7906.8900 16966.3000 17163.3000 10154.7000
## 84 22400.3000 20278.3000 40972.7000 6644.2900 18426.8000
## 85 24409.5000 13155.4000 9450.3700 13812.0000 24152.1000
## 86 19157.1000 43416.7000 36306.8000 99004.1000 42024.1000
## 87 39811.0000 9888.6600 14198.4000 19990.6000 23522.7000
## 88 19596.6000 42241.2000 24671.2000 15462.2000 25149.3000
## 89 50379.4000 23058.8000 33351.7000 52577.6000 42374.7000
## 90 35076.3000 21508.2000 26046.8000 35011.9000 26635.7000
## 91 22688.6000 12458.0000 25558.6000 19219.7000 35674.1000
## 92 53451.1000 20312.5000 22668.2000 25863.9000 21354.3000
## 93 72546.3000 43058.3000 163575.0000 31265.9000 44310.4000
## 94 108820.0000 33975.1000 33142.3000 30732.0000 33303.9000
## 95 33326.4000 39073.1000 29402.4000 80977.3000 35905.0000
## 96 22873.3000 52946.7000 24182.1000 38050.7000 32183.3000
## 97 7097.4100 30096.9000 14784.4000 37514.9000 39073.1000
## 98 42813.4000 62303.3000 40180.8000 14836.3000 40159.8000
## 99 29560.8000 33961.3000 27961.4000 8305.3600 72671.9000
## 100 16746.1000 21192.7000 18136.6000 14467.5000 22891.3000
## Jun Jul Aug Sep Oct
## 1 20226.3000 118473.0000 48361.1000 72926.1000 20686.9000
## 2 38531.9000 32600.9000 91260.0000 17485.3000 47286.9000
## 3 25819.2000 29580.5000 30564.2000 2048.0700 202415.0000
## 4 27340.8000 21799.3000 23751.7000 9499.5100 32436.9000
## 5 19307.4000 44764.3000 33548.9000 79874.9000 10858.7000
## 6 84240.5000 27898.0000 59964.9000 42826.0000 73929.0000
## 7 16859.2000 43753.1000 49215.4000 36389.5000 29151.0000
## 8 21259.7000 8192.3000 16222.1000 22985.1000 46688.6000
## 9 7503.0300 12127.1000 24584.8000 17543.6000 45507.8000
## 10 76889.1000 37515.1000 28673.0000 15683.3000 26483.5000
## 11 46198.3000 46120.9000 17248.2000 31973.4000 43054.1000
## 12 63680.5000 60275.9000 65129.3000 39761.0000 64227.8000
## 13 33858.5000 36486.2000 63189.6000 54452.7000 99512.3000
## 14 47956.1000 25210.6000 10507.0000 38541.2000 99331.6000
## 15 29064.4000 13943.9000 32972.7000 35956.5000 17383.8000
## 16 50048.5000 56957.5000 79917.0000 51807.6000 22292.7000
## 17 21607.1000 20476.3000 72915.8000 26259.3000 18190.4000
## 18 41114.6000 35416.2000 50865.7000 18958.1000 36136.0000
## 19 72072.4000 24276.5000 62656.8000 74221.5000 30459.3000
## 20 66534.5000 40859.5000 22170.1000 27967.0000 17683.7000
## 21 5949.7100 28772.6000 23962.1000 63100.4000 24478.9000
## 22 14583.2000 27353.4000 35208.3000 41337.8000 70126.9000
## 23 22813.3000 18878.9000 20904.7000 48709.6000 30635.8000
## 24 22424.9000 36342.5000 15557.1000 19385.0000 24730.9000
## 25 14664.4000 44552.5000 28441.7000 107186.0000 88482.4000
## 26 196762.0000 26082.6000 20450.8000 56919.5000 43198.4000
## 27 12605.3000 19262.1000 72936.3000 30495.6000 51331.5000
## 28 43902.6000 71682.6000 61360.6000 20270.8000 20398.7000
## 29 39700.9000 53723.9000 43383.9000 39821.0000 34006.6000
## 30 16077.9000 48194.3000 48848.6000 34453.4000 41364.0000
## 31 35102.7000 21580.3000 93001.6000 10941.4000 30959.3000
## 32 45299.8000 23359.9000 17668.9000 50543.3000 33108.9000
## 33 35534.9000 16946.3000 43679.7000 80299.1000 17588.1000
## 34 66044.6000 62407.5000 47730.1000 69749.6000 58210.3000
## 35 68176.4000 55075.9000 84589.8000 41924.4000 99445.9000
## 36 16171.2000 51546.9000 45273.8000 57182.8000 49951.9000
## 37 40258.7000 55500.6000 48143.2000 20588.1000 69871.1000
## 38 43816.0000 72835.7000 24343.9000 67663.7000 78872.1000
## 39 72429.8000 51357.0000 94076.4000 90451.2000 83463.3000
## 40 46756.3000 40360.4000 21605.1000 30797.3000 71734.2000
## 41 23355.6000 14257.1000 33225.4000 35830.5000 38728.2000
## 42 39868.5000 30738.0000 48285.9000 19923.4000 38494.6000
## 43 66882.7000 34765.8000 30365.2000 44959.2000 28787.4000
## 44 24272.2000 32046.9000 24685.6000 26422.6000 33908.7000
## 45 105020.0000 79333.6000 53673.3000 47882.1000 37357.9000
## 46 37223.2000 43887.5000 30198.6000 38426.7000 56112.7000
## 47 24199.0000 12877.1000 29418.4000 22034.3000 62493.7000
## 48 6509.4300 79796.3000 24646.4000 3962.8400 41051.8000
## 49 7753.4000 16663.9000 16084.5000 10880.4000 9248.0300
## 50 20025.3000 43703.3000 6561.3800 18803.2000 8978.3100
## 51 13415.8000 10025.1000 14141.4000 23044.0000 3656.8400
## 52 46819.3000 42148.7000 95791.8000 44672.8000 58082.6000
## 53 9756.7600 26706.3000 24001.3000 26919.9000 30010.6000
## 54 41714.1000 24047.1000 14483.5000 22787.0000 10628.0000
## 55 27785.7000 51251.3000 42237.4000 58493.1000 63140.2000
## 56 26033.1000 33617.0000 24760.4000 19640.1000 15133.2000
## 57 25239.6000 18979.9000 41818.9000 21505.1000 23854.1000
## 58 18801.9000 26251.1000 22130.9000 21367.0000 42634.8000
## 59 55800.9000 49380.0000 161534.0000 24183.1000 44713.5000
## 60 37558.3000 45295.3000 43230.3000 30565.1000 38061.3000
## 61 42174.2000 38585.5000 30794.8000 79966.8000 35457.0000
## 62 23016.9000 52286.0000 40603.3000 43045.5000 19404.9000
## 63 7008.8500 27716.9000 14599.9000 37046.8000 38585.5000
## 64 42960.0000 72999.6000 30289.5000 17121.6000 39884.8000
## 65 20710.7000 28629.0000 30468.3000 26862.5000 8201.7200
## 66 69751.1000 22629.5000 18642.1000 18524.3000 23608.1000
## 67 45430.0000 79006.2000 89159.6000 48615.5000 63515.9000
## 68 28212.5000 41356.9000 42645.4000 89926.3000 63545.1000
## 69 57481.0000 34321.0000 63259.0000 6864.1900 18874.4000
## 70 29537.1000 31680.9000 72546.3000 42712.9000 41193.5000
## 71 57068.6000 31128.4000 2919.1300 15572.5000 44369.6000
## 72 90835.7000 48994.6000 60954.4000 53737.5000 81124.8000
## 73 87648.6000 90682.9000 45944.0000 77209.2000 47483.2000
## 74 85761.6000 39977.0000 81862.9000 47860.4000 22374.7000
## 75 25597.7000 19756.8000 35329.3000 45829.0000 27623.1000
## 76 28735.3000 21618.8000 1508.0300 16315.7000 2534.4700
## 77 9530.4500 18584.3000 71755.1000 68750.9000 35002.4000
## 78 62848.6000 126737.0000 31147.5000 48668.0000 83228.0000
## 79 21946.9000 57312.8000 139216.0000 7041.6700 157969.0000
## 80 34715.6000 50281.9000 22787.1000 49271.1000 26120.3000
## 81 65975.7000 80443.5000 85255.8000 83603.0000 55225.1000
## 82 26209.4000 31933.6000 28870.0000 25304.4000 15823.4000
## 83 8517.3200 9103.1500 40469.3000 51885.2000 14605.2000
## 84 23619.3000 2561.6900 13692.3000 12268.1000 12906.7000
## 85 3054.3900 23131.1000 42862.6000 71234.9000 10970.0000
## 86 60035.2000 47602.7000 54956.0000 52077.7000 16706.4000
## 87 30340.2000 42361.3000 20278.3000 25134.7000 66149.7000
## 88 4660.7300 35451.1000 32059.3000 18895.5000 43113.2000
## 89 45403.8000 71002.2000 42099.2000 21729.3000 14311.2000
## 90 22013.5000 15324.4000 31967.5000 44866.2000 36033.7000
## 91 24482.7000 24521.1000 15048.0000 21470.2000 30966.2000
## 92 52831.4000 23399.0000 27192.8000 60905.1000 81113.3000
## 93 31018.6000 41890.8000 34575.8000 50.6958 28829.6000
## 94 35155.7000 48405.7000 36961.1000 31444.0000 34672.6000
## 95 25329.8000 56329.3000 19090.6000 25338.5000 20820.9000
## 96 26637.6000 13570.1000 10938.5000 14222.4000 16448.0000
## 97 70333.6000 18535.4000 34108.3000 43003.7000 31248.9000
## 98 60455.3000 14100.8000 23922.3000 33390.6000 77259.2000
## 99 60733.5000 55969.6000 23430.3000 20508.0000 64417.8000
## 100 24816.7000 65168.7000 64258.2000 27798.7000 43111.0000
## Nov Dec
## 1 6553.8400 80820.6000
## 2 30673.4000 36847.1000
## 3 32997.5000 17761.7000
## 4 98307.6000 52021.1000
## 5 53593.1000 40974.0000
## 6 28130.1000 56501.5000
## 7 8710.2200 26558.1000
## 8 21959.9000 44114.6000
## 9 33643.7000 37676.5000
## 10 28437.2000 27633.5000
## 11 5208.2700 28447.5000
## 12 6144.2200 79752.2000
## 13 39716.1000 12288.4000
## 14 3470.3200 65136.5000
## 15 19164.9000 26939.8000
## 16 23005.0000 75025.8000
## 17 12961.8000 129546.0000
## 18 23580.9000 41055.6000
## 19 50817.7000 72118.7000
## 20 24054.0000 73623.1000
## 21 26100.2000 17783.1000
## 22 14619.6000 13315.4000
## 23 57047.0000 41961.1000
## 24 34443.6000 19293.5000
## 25 12862.3000 28379.9000
## 26 58638.9000 42693.2000
## 27 33775.1000 51930.3000
## 28 21962.7000 59690.7000
## 29 89470.3000 38601.4000
## 30 94359.8000 35359.8000
## 31 6437.3700 27138.4000
## 32 35389.6000 44394.6000
## 33 19059.3000 13340.6000
## 34 58687.6000 80558.9000
## 35 22239.8000 77639.0000
## 36 47279.2000 40565.4000
## 37 55936.9000 51357.7000
## 38 87895.7000 57961.7000
## 39 133726.0000 72177.3000
## 40 80482.0000 91252.7000
## 41 33476.1000 27682.2000
## 42 39817.1000 33117.2000
## 43 16060.6000 15639.1000
## 44 55442.3000 20191.7000
## 45 34279.3000 54320.0000
## 46 28773.1000 48676.5000
## 47 76274.8000 85708.7000
## 48 35641.7000 28147.5000
## 49 8282.8500 33688.6000
## 50 2624.9000 11786.6000
## 51 9543.4400 37770.3000
## 52 45689.4000 68104.1000
## 53 44529.4000 20025.3000
## 54 35116.2000 40144.5000
## 55 41228.7000 21781.8000
## 56 31434.1000 31448.4000
## 57 14199.7000 22062.9000
## 58 21461.9000 19275.7000
## 59 30265.2000 33824.6000
## 60 38751.1000 48249.8000
## 61 25096.7000 51550.8000
## 62 8381.9700 18998.1000
## 63 56140.7000 22502.4000
## 64 19315.7000 19408.1000
## 65 71765.0000 62953.1000
## 66 17635.2000 19391.7000
## 67 62989.7000 48422.1000
## 68 55095.8000 82160.2000
## 69 52201.8000 45995.3000
## 70 48517.4000 44047.3000
## 71 70150.1000 81603.8000
## 72 55102.1000 87654.3000
## 73 34315.8000 29216.8000
## 74 50379.4000 14231.2000
## 75 129258.0000 40930.4000
## 76 17179.3000 72293.1000
## 77 51853.3000 29461.8000
## 78 226021.0000 118948.0000
## 79 33337.0000 51010.9000
## 80 25054.6000 17405.7000
## 81 40009.6000 57600.8000
## 82 12636.9000 24703.6000
## 83 20602.9000 25372.7000
## 84 18512.9000 26726.4000
## 85 30435.2000 37178.6000
## 86 20748.7000 27768.6000
## 87 33938.0000 30417.5000
## 88 39734.1000 50219.5000
## 89 15335.1000 32661.5000
## 90 30417.5000 10189.9000
## 91 31258.1000 25305.5000
## 92 18741.1000 29729.2000
## 93 32454.1000 59389.8000
## 94 52256.7000 29068.8000
## 95 25580.2000 20763.6000
## 96 15948.5000 17476.3000
## 97 8305.3600 17379.8000
## 98 114709.0000 34482.6000
## 99 17968.9000 22345.6000
## 100ddata12 <- decompose(tsdata12, "multiplicative")
plot(ddata12)
plot(ddata12$seasonal)
plot(ddata12$trend)
plot(trace$avg_wage,type ="l",main = "Average salary for Two or more races",xlab= "Year",ylab="Average Salary")
class(tsdata12)## [1] "ts"mymodel12 <- auto.arima(tsdata12)
mymodel12## Series: tsdata12
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 sar1 mean
## 0.8923 -0.7433 0.0038 40023.31
## s.e. 0.0347 0.0524 0.0300 1797.61
##
## sigma^2 estimated as 684363021: log likelihood=-13884.05
## AIC=27778.1 AICc=27778.15 BIC=27803.54auto.arima(tsdata12,ic="aic",trace=TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 27774.69
## ARIMA(0,0,0) with non-zero mean : 27896.84
## ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 27818.73
## ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 27846.13
## ARIMA(0,0,0) with zero mean : 29256.79
## ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 27783.29
## ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 27772.87
## ARIMA(2,0,2) with non-zero mean : 27781.31
## ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 27776.63
## ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 27777.27
## ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 27771.81
## ARIMA(1,0,2) with non-zero mean : 27777.67
## ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 27772.27
## ARIMA(1,0,2)(1,0,1)[12] with non-zero mean : 27773.79
## ARIMA(1,0,2)(0,0,1)[12] with non-zero mean : 27779.66
## ARIMA(1,0,2)(2,0,1)[12] with non-zero mean : 27772.75
## ARIMA(0,0,2)(1,0,0)[12] with non-zero mean : 27806.11
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 27770.84
## ARIMA(1,0,1) with non-zero mean : 27776.35
## ARIMA(1,0,1)(2,0,0)[12] with non-zero mean : 27771.61
## ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 27772.83
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 27778.33
## ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 27772.35
## ARIMA(0,0,1)(1,0,0)[12] with non-zero mean : 27835.5
## ARIMA(2,0,1)(1,0,0)[12] with non-zero mean : 27770.9
## ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 27884.64
## ARIMA(2,0,0)(1,0,0)[12] with non-zero mean : 27790.96
## ARIMA(1,0,1)(1,0,0)[12] with zero mean : Inf
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 27778.1
##
## Best model: ARIMA(1,0,1)(1,0,0)[12] with non-zero mean## Series: tsdata12
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean
##
## Coefficients:
## ar1 ma1 sar1 mean
## 0.8923 -0.7433 0.0038 40023.31
## s.e. 0.0347 0.0524 0.0300 1797.61
##
## sigma^2 estimated as 684363021: log likelihood=-13884.05
## AIC=27778.1 AICc=27778.15 BIC=27803.54library(tseries)
adf.test(mymodel12$residuals)## Warning in adf.test(mymodel12$residuals): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: mymodel12$residuals
## Dickey-Fuller = -10.529, Lag order = 10, p-value = 0.01
## alternative hypothesis: stationaryplot.ts(mymodel12$residuals)
acf(ts(mymodel12$residuals),main = "ACF Residual")
pacf(ts(mymodel12$residuals),main = "PACF Residual")
myforecast12 <- forecast(mymodel12, level =c (95), h= 3*12)
plot(myforecast12,main = "Salary forecasting for Two or more races in next 3years",xlab = "Time", ylab ="Average salary")
myforecast12[["mean"]]## Jan Feb Mar Apr May Jun Jul
## 100
## 101 39627.19 39677.18 39695.23 39707.76 39763.19 39791.51 39962.88
## 102 39943.30 39951.94 39959.55 39966.33 39972.55 39978.02 39983.45
## 103 40003.00 40005.18 40007.14 40008.88 40010.43 40011.82 40013.06
## Aug Sep Oct Nov Dec
## 100 39552.97 39611.19
## 101 39976.18 39853.24 39924.48 39922.93 39933.76
## 102 39987.76 39991.11 39994.77 39997.80 40000.55
## 103 40014.16 40015.14 40016.02Bias Within Job Classes: Computer programmers
newsal <- drop_na(Sal)
bsal <- subset(newsal, soc_name == "Computer programmers",select = c(avg_wage_ft,year,sex_name,num_ppl,soc_name))
head(bsal)ggplot(data= bsal, mapping= aes(x= factor(year), y= avg_wage_ft,fill= sex_name))+geom_bar(stat = "identity",position = "dodge2")+labs(x= "year",y= "Average salary",title = "Year vs Average Salaryfor Computer Programmers")
ggplot(data= bsal, mapping= aes(x= factor(year),y= num_ppl,fill= sex_name))+geom_bar(stat= "identity",position = "dodge2")+labs(x= "year",y= "No. of Employees",title = "Year vs No.of Employees for Computer Programmers")
Bias Within Job Classes: Mechanical engineers
newsal <- drop_na(Sal)
mesal <- subset(newsal, soc_name == "Mechanical engineers",select = c(avg_wage_ft,year,sex_name,num_ppl,soc_name))
head(mesal)ggplot(data= mesal, mapping= aes(x= factor(year), y= avg_wage_ft,fill= sex_name))+geom_bar(stat = "identity",position = "dodge2")+labs(x= "year",y= "Average salary",title = "Year vs Average Salary for Mechanical engineers")
ggplot(data= mesal, mapping= aes(x= factor(year),y= num_ppl,fill= sex_name))+geom_bar(stat= "identity",position = "dodge2")+labs(x= "year",y= "No. of Employees",title = "Year vs No.of Employees for Mechanical engineers")
#Bias Within Job Classes: Electrical & electronics engineers
newsal <- drop_na(Sal)
eesal <- subset(newsal, soc_name == "Electrical & electronics engineers",select =
c(avg_wage_ft,year,sex_name,num_ppl,soc_name))
head(eesal)ggplot(data= eesal, mapping= aes(x= factor(year), y= avg_wage_ft,fill= sex_name))+geom_bar(stat = "identity",position = "dodge2")+labs(x= "year",y= "Average salary",title = "Year vs Average Salary for Electrical & electronics engineers")
ggplot(data= eesal, mapping= aes(x= factor(year),y= num_ppl,fill= sex_name))+geom_bar(stat= "identity",position = "dodge2")+labs(x= "year",y= "No. of Employees",title = "Year vs No.of Employees for Electrical & electronics engineers")
#Bias Within Job Classes: Civil engineers
newsal <- drop_na(Sal)
csal <- subset(newsal, soc_name == "Civil engineers",select =
c(avg_wage_ft,year,sex_name,num_ppl,soc_name))
head(csal)ggplot(data= csal, mapping= aes(x= factor(year), y= avg_wage_ft,fill= sex_name))+geom_bar(stat = "identity",position = "dodge2")+labs(x= "year",y= "Average salary",title = "Year vs Average Salary for Civil engineers")
ggplot(data= csal, mapping= aes(x= factor(year),y= num_ppl,fill= sex_name))+geom_bar(stat= "identity",position = "dodge2")+labs(x= "year",y= "No. of Employees",title = "Year vs No.of Employees for Civil engineers")
Bias Within Job Classes: Aerospace engineers
newsal <- drop_na(Sal)
asal <- subset(newsal, soc_name == "Aerospace engineers",select =
c(avg_wage_ft,year,sex_name,num_ppl,soc_name))
head(asal)ggplot(data= asal, mapping= aes(x= factor(year), y= avg_wage_ft,fill= sex_name))+geom_bar(stat = "identity",position = "dodge2")+labs(x= "year",y= "Average salary",title = "Year vs Average Salary for Aerospace engineers")
ggplot(data= asal, mapping= aes(x= factor(year),y= num_ppl,fill= sex_name))+geom_bar(stat= "identity",position = "dodge2")+labs(x= "year",y= "No. of Employees",title = "Year vs No.of Employees for Aerospace engineers")
#predicted monthly salary by races
ggplot(data= predictedrace,aes(x= factor(Year),y=mean))+geom_bar(stat="identity",position = "dodge",aes(fill=Race))+labs(title = "Predicted mean by racial group",x="year",y="Average salary")
##predicted monthly salary by races
library(plotly)## Warning: package 'plotly' was built under R version 3.5.3##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(gganimate)## Warning: package 'gganimate' was built under R version 3.5.3predg <- subset(predictedrace,sex== "male" | sex=="female",select=c(year1,gender_mean,sex))
p<- ggplot(data= predg,aes(x= year1,y=gender_mean))+geom_bar(stat="identity",position = "dodge",aes(fill=sex))+labs(title = "Predicted mean by gender",x="year",y="Average salary")+transition_states(sex,transition_length=2,state_length=1)+enter_fade()+exit_shrink()+ease_aes("sine-in-out")
p
unem <- subset(predictedrace,select=c(year2,Unemploy))
unem<- drop_na(unem)
p<- ggplot(data= unem,aes(x= year2,y=Unemploy))+geom_bar(stat="identity", fill="Navy blue")+labs(title = "Predicted Unemployment rate",x="year",y="Unemployment rate")
p